.claude/docs/technical/PLACEHOLDER_REGISTRY_FRAMEWORK.md

@@ -92,16 +92,10 @@ registry = get_registry()
 
 **Package:** `backend/placeholder_registrations/`
 
-**Struktur:**
+**Struktur:** Vollständige Cluster-Module (u. a. Ernährung, Körper, Aktivität, Schlaf,
-```
+Vitalwerte, Profil/Zeitraum, Phase-0b-Ziele, Korrelationen); siehe `__init__.py` für die
-placeholder_registrations/
+Import-Liste. **Anzahl:** 114 Platzhalter, identisch zu `PLACEHOLDER_MAP` in
-├── __init__.py              # Auto-Import aller Registrations
+`placeholder_resolver.py`.
-├── nutrition_part_a.py      # Nutrition Basis-Metriken (4 Placeholder)
-├── nutrition_part_b.py      # Protein-Ziele (5 Placeholder) - TODO
-├── body_metrics.py          # Körper-Metriken - TODO
-├── activity_metrics.py      # Aktivitäts-Metriken - TODO
-└── ...                      # Weitere Cluster
-```
 
 **Auto-Registration:**
 - Import des Package triggert automatische Registrierung aller Placeholder

.claude/docs/working/phase-0c-placeholder-migration-analysis.md

@@ -7,7 +7,7 @@
 
 ## Gesamt-Übersicht
 
-**Aktuelle Platzhalter:** 116
+**Aktuelle Platzhalter:** 114 (PLACEHOLDER_MAP / Registry)
 **Nach Phase 0c Migration:**
 - ✅ **Bleiben einfach (kein Data Layer):** 8 Platzhalter
 - 🔄 **Gehen zu Data Layer:** 108 Platzhalter

CLAUDE.md

@@ -107,7 +107,7 @@ frontend/src/
 
 ### Updates (11.04.2026 - Placeholder Phase A)
 
-- **`main.py`:** `import placeholder_registrations` beim Start, damit die Registry (48 Keys) und `get_placeholder_catalog()` ohne vorherigen Export-Request konsistent sind.
+- **`main.py`:** `import placeholder_registrations` beim Start, damit die Registry (**114 Keys**, deckungsgleich `PLACEHOLDER_MAP`) und `get_placeholder_catalog()` ohne vorherigen Export-Request konsistent sind.
 - **`placeholder_resolver.py`:** `{{top_goal_progress_pct}}` nutzt `_safe_int` statt `_safe_str` (Verdrahtung zu `scores.get_top_priority_goal` korrigiert).
 
 ### Updates (11.04.2026 - Gitea #75, nutrition_score Registry)
@@ -115,10 +115,11 @@ frontend/src/
 - **Gitea #75** (offen): Zucker/Ballaststoffe/Lebensmittelqualität, automatisches Lebensmittelprofil, später Mahlzeiten-Timing/Abgleich mit Training — http://192.168.2.144:3000/Lars/mitai-jinkendo/issues/75
 - **`nutrition_score`:** Registry in `backend/placeholder_registrations/nutrition_score.py`, Import in `placeholder_registrations/__init__.py`; Legacy-Duplikat unter „Scores“ im Platzhalter-Katalog entfernt.
 
-### Updates (11.04.2026 - Ernährung: eine TDEE-/Tageslogik)
+### Updates (11.04.2026 - Ernährung: TDEE, Bilanz, Kalorien-Score)
 
-- **`data_layer/nutrition_metrics.py`:** TDEE für Bilanz = **aktuelles Gewicht × 32,5 kcal/kg** (`estimate_tdee_kcal_from_latest_weight`); `get_energy_balance_data` und `calculate_energy_balance_7d` nutzen **tägliche kcal-Summen** (nicht Rohzeilen). Makro-Durchschnitte über **Tagesmittel**; `protein_adequacy_28d`, `macro_consistency_score`, `get_protein_adequacy_data`, `get_macro_consistency_data` auf **Kalendertag** umgestellt. Entfernt: festes **2500 kcal** in `get_energy_balance_data`.
+- **`data_layer/nutrition_metrics.py`:** TDEE für Bilanz: primär **Mifflin–St Jeor BMR × PAL 1,55**, wenn Profil (Größe, Geschlecht, DOB) und Gewicht vorhanden; sonst Fallback **kg × 32,5** (`estimate_tdee_kcal_from_latest_weight`). `get_energy_balance_data` / `calculate_energy_balance_7d` nutzen **tägliche kcal-Summen**. **`_score_calorie_adherence`** (Komponente von `calculate_nutrition_score`) wertet die 7-Tage-Bilanz nach **`profiles.goal_mode`** aus (weight_loss vs. strength/recomposition vs. maintenance/health/endurance).
 - **`routers/charts.py`:** `/charts/energy-balance` und Protein-Timeline nutzen dieselbe TDEE-/Tageslogik; ohne `weight_log` liefert Energiebilanz-Chart eine klare Fehlermeldung. Adherence-Endpoint: Kcal-CV über **Tages-Summen**.
+- **Doku:** Normative Platzhalter-Zahl **114** (`docs/PLACEHOLDER_*.md`); `placeholder_metadata_complete.py` als **Legacy** gekennzeichnet — maßgeblich `placeholder_registrations/` + `PLACEHOLDER_REGISTRY_FRAMEWORK.md`.
 
 ### GUI / Informationsarchitektur (Abnahme dieser Iteration, 2026-04-05)
 

backend/data_layer/nutrition_metrics.py

@@ -25,14 +25,43 @@ from datetime import datetime, timedelta, date
 from db import get_db, get_cursor, r2d
 from data_layer.utils import calculate_confidence, safe_float, safe_int
 
-# Single TDEE rule for placeholders, charts, and warnings (kcal/day = kg * factor).
+# Fallback TDEE (kcal/day) when demographics for Mifflin–St Jeor are incomplete.
-# Replaces legacy fixed 2500 kcal so all consumers stay aligned.
 TDEE_KCAL_PER_KG_BODYWEIGHT = 32.5
+# PAL applied to MSJ BMR when height, sex, dob and weight are available (moderate activity).
+TDEE_PAL_MODERATE = 1.55
+
+
+def _age_years_from_dob(dob) -> Optional[int]:
+    if dob is None:
+        return None
+    try:
+        if isinstance(dob, str):
+            birth = datetime.strptime(dob[:10], "%Y-%m-%d").date()
+        else:
+            birth = dob
+        today = date.today()
+        return today.year - birth.year - ((today.month, today.day) < (birth.month, birth.day))
+    except Exception:
+        return None
+
+
+def _mifflin_st_jeor_bmr_kcal(
+    weight_kg: float, height_cm: float, age_years: int, sex_is_male: bool
+) -> float:
+    if sex_is_male:
+        return 10.0 * weight_kg + 6.25 * height_cm - 5.0 * age_years + 5.0
+    return 10.0 * weight_kg + 6.25 * height_cm - 5.0 * age_years - 161.0
 
 
 def estimate_tdee_kcal_from_latest_weight(profile_id: str) -> Optional[float]:
     """
-    Estimated TDEE (kcal/day) from latest body weight.
+    Estimated TDEE (kcal/day).
+
+    Primary: Mifflin–St Jeor BMR × TDEE_PAL_MODERATE when latest weight plus
+    profiles.height, profiles.sex, profiles.dob are usable.
+
+    Fallback: latest weight (kg) × TDEE_KCAL_PER_KG_BODYWEIGHT (legacy heuristic).
+
     Returns None if no weight on record.
     """
     with get_db() as conn:
@@ -42,10 +71,41 @@ def estimate_tdee_kcal_from_latest_weight(profile_id: str) -> Optional[float]:
                WHERE profile_id=%s ORDER BY date DESC LIMIT 1""",
             (profile_id,),
         )
-        row = cur.fetchone()
+        wrow = cur.fetchone()
-        if not row or row["weight"] is None:
+        if not wrow or wrow["weight"] is None:
             return None
-        return float(row["weight"]) * TDEE_KCAL_PER_KG_BODYWEIGHT
+        weight_kg = float(wrow["weight"])
+
+        cur.execute(
+            "SELECT height, sex, dob FROM profiles WHERE id=%s",
+            (profile_id,),
+        )
+        prow = cur.fetchone()
+
+    if prow and prow.get("height") and prow.get("sex") is not None and prow.get("dob"):
+        height_cm = float(prow["height"])
+        age = _age_years_from_dob(prow["dob"])
+        if age is not None and 10 < age < 120 and height_cm > 50:
+            sex_raw = str(prow["sex"]).strip().lower()
+            sex_is_male = sex_raw in ("m", "male", "männlich", "mann")
+            bmr = _mifflin_st_jeor_bmr_kcal(weight_kg, height_cm, age, sex_is_male)
+            if bmr > 400:
+                return bmr * TDEE_PAL_MODERATE
+
+    return weight_kg * TDEE_KCAL_PER_KG_BODYWEIGHT
+
+
+def _get_profile_goal_mode(profile_id: str) -> str:
+    """Strategic goal_mode from profiles (Phase 0a); defaults to health."""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("SELECT goal_mode FROM profiles WHERE id=%s", (profile_id,))
+        row = cur.fetchone()
+        if row and row.get("goal_mode"):
+            g = str(row["goal_mode"]).strip().lower()
+            if g:
+                return g
+    return "health"
 
 
 def get_nutrition_average_data(
@@ -224,7 +284,7 @@ def get_energy_balance_data(
     Energy balance (intake - estimated expenditure), kcal/day.
 
     Intake: mean of daily total kcal (sum per calendar day).
-    TDEE: latest weight (kg) * TDEE_KCAL_PER_KG_BODYWEIGHT (same rule as placeholders).
+    TDEE: estimate_tdee_kcal_from_latest_weight (MSJ × PAL oder kg-Fallback).
     """
     with get_db() as conn:
         cur = get_cursor(conn)
@@ -834,32 +894,58 @@ def calculate_nutrition_score(profile_id: str, focus_weights: Optional[Dict] = N
 
 
 def _score_calorie_adherence(profile_id: str) -> Optional[int]:
-    """Score calorie target adherence (0-100)"""
+    """Score calorie target adherence (0–100) using 7d balance vs profiles.goal_mode."""
-    # Check for energy balance goal
-    # For now, use energy balance calculation
     balance = calculate_energy_balance_7d(profile_id)
-
     if balance is None:
         return None
 
-    # Score based on whether deficit/surplus aligns with goal
+    mode = _get_profile_goal_mode(profile_id)
-    # Simplified: assume weight loss goal = deficit is good
+    b = float(balance)
-    # TODO: Check actual goal type
 
-    abs_balance = abs(balance)
+    def _weight_loss(x: float) -> int:
+        if -550 <= x <= -250:
+            return 100
+        if x > 450:
+            return 38
+        if -750 <= x < -550 or -250 < x <= 120:
+            return 82
+        if x < -1200:
+            return 52
+        if -950 <= x < -750 or 120 < x <= 350:
+            return 68
+        return 58
 
-    # Moderate deficit/surplus = good
+    def _surplus_friendly(x: float) -> int:
-    if 200 <= abs_balance <= 500:
+        if 80 <= x <= 480:
-        return 100
+            return 100
-    elif 100 <= abs_balance <= 700:
+        if -120 <= x < 80 or 480 < x <= 700:
-        return 85
+            return 86
-    elif abs_balance <= 900:
+        if -380 <= x < -120:
-        return 70
+            return 68
-    elif abs_balance <= 1200:
+        if x > 850:
-        return 55
+            return 54
-    else:
+        if x < -650:
+            return 44
+        return 72
+
+    def _maintenance(x: float) -> int:
+        a = abs(x)
+        if a <= 200:
+            return 100
+        if a <= 400:
+            return 84
+        if a <= 650:
+            return 70
+        if a <= 900:
+            return 55
         return 40
 
+    if mode == "weight_loss":
+        return _weight_loss(b)
+    if mode in ("strength", "recomposition"):
+        return _surplus_friendly(b)
+    return _maintenance(b)
+
 
 def _score_macro_balance(profile_id: str) -> Optional[int]:
     """Score macro balance (0-100)"""

backend/placeholder_metadata_complete.py

@@ -1,11 +1,10 @@
 """
-Complete Placeholder Metadata Definitions
+Complete Placeholder Metadata Definitions (Legacy / Normativ v1)
 
-This module contains manually curated, complete metadata for all 116 placeholders.
+Hinweis (2026-04): **Verbindliche Metadaten-Pflege** erfolgt über
-It combines automatic extraction with manual annotation to ensure 100% normative compliance.
+`backend/placeholder_registrations/` + `placeholder_registry.py` (114 Keys, deckungsgleich
-
+mit `PLACEHOLDER_MAP`). Dieses Modul bleibt für ältere Generator-/Export-Pfade und
-IMPORTANT: This is the authoritative source for placeholder metadata.
+Tests; neue Platzhalter hier nicht mehr duplizieren.
-All new placeholders MUST be added here with complete metadata.
 """
 from placeholder_metadata import (
     PlaceholderMetadata,
@@ -28,7 +27,7 @@ from typing import List
 
 def get_all_placeholder_metadata() -> List[PlaceholderMetadata]:
     """
-    Returns complete metadata for all 116 placeholders.
+    Returns complete metadata for all 114 placeholders (Registry ist maßgeblich).
 
     This is the authoritative, manually curated source.
     """
@@ -476,7 +475,7 @@ def get_all_placeholder_metadata() -> List[PlaceholderMetadata]:
             notes=["Quadrant-Logik basiert auf FM/LBM Delta-Vorzeichen"],
         ),
 
-        # NOTE: Continuing with all 116 placeholders would make this file very long.
+        # NOTE: Continuing with all 114 placeholders would make this file very long.
         # For brevity, I'll create a separate generator that fills all remaining placeholders.
         # The pattern is established above - each placeholder gets full metadata.
     ]

backend/placeholder_registrations/nutrition_part_a.py

@@ -53,6 +53,13 @@ def register_nutrition_part_a():
         "layer_1_decision": "Data Layer (nutrition_metrics.get_nutrition_average_data)",
         "layer_2a_decision": "Placeholder Resolver (formatting only)",
         "architecture_alignment": "Phase 0c Multi-Layer Architecture conform",
+        "minimum_data_requirements": (
+            "Mind. ein Kalendertag mit nutrition_log im Fenster; Mittelwerte aus täglicher Aggregation. "
+            "Confidence über calculate_confidence(day_count, days) in get_nutrition_average_data."
+        ),
+        "quality_filter_policy": (
+            "Kein Outlier-Filter auf Tagesaggregaten; leere Tage fehlen in der Aggregation (kein Imputing)."
+        ),
     }
 
     # Common evidence for shared fields
@@ -73,8 +80,8 @@ def register_nutrition_part_a():
         "layer_2b_reuse_possible": EvidenceType.TO_VERIFY,  # not verified in charts
         "architecture_alignment": EvidenceType.CODE_DERIVED,  # imports from data_layer
         "issue_53_alignment": EvidenceType.MIXED,  # layer separation visible, issue conformity derived
-        "minimum_data_requirements": EvidenceType.UNRESOLVED,  # not explicit in code
+        "minimum_data_requirements": EvidenceType.CODE_DERIVED,
-        "quality_filter_policy": EvidenceType.UNRESOLVED,  # not implemented
+        "quality_filter_policy": EvidenceType.CODE_DERIVED,
     }
 
     # ── kcal_avg ──────────────────────────────────────────────────────────────
@@ -94,8 +101,6 @@ def register_nutrition_part_a():
         known_limitations="nur Intake, kein Bedarf; sagt allein nichts über Zielpassung",
         layer_2b_reuse_possible=None,  # to_verify - not checked in chart code
         issue_53_alignment="Layer separation established",
-        minimum_data_requirements=None,  # unresolved
-        quality_filter_policy=None,  # unresolved
         **common_metadata
     )
 
@@ -131,8 +136,6 @@ def register_nutrition_part_a():
         ),
         layer_2b_reuse_possible=None,
         issue_53_alignment="Layer separation established",
-        minimum_data_requirements=None,
-        quality_filter_policy=None,
         **common_metadata
     )
 
@@ -165,8 +168,6 @@ def register_nutrition_part_a():
         ),
         layer_2b_reuse_possible=None,
         issue_53_alignment="Layer separation established",
-        minimum_data_requirements=None,
-        quality_filter_policy=None,
         **common_metadata
     )
 
@@ -196,8 +197,6 @@ def register_nutrition_part_a():
         known_limitations="meist im Gesamtkontext der Makroverteilung relevant",
         layer_2b_reuse_possible=None,
         issue_53_alignment="Layer separation established",
-        minimum_data_requirements=None,
-        quality_filter_policy=None,
         **common_metadata
     )
 

backend/placeholder_registrations/nutrition_part_c.py

@@ -113,7 +113,7 @@ energy_balance_metadata = PlaceholderMetadata(
     resolver_function="_safe_float('energy_balance_7d', pid, decimals=0)",
     data_layer_module="backend/data_layer/nutrition_metrics.py",
     data_layer_function="calculate_energy_balance_7d",
-    source_tables=["nutrition_log", "weight_log"],
+    source_tables=["nutrition_log", "weight_log", "profiles"],
 
     # Semantic
     semantic_contract="Liefert die geschätzte Energiebilanz über 7 Tage als Differenz zwischen durchschnittlicher Energieaufnahme und geschätztem TDEE (Total Daily Energy Expenditure). Positiver Wert = Überschuss, Negativer Wert = Defizit.",
@@ -127,11 +127,14 @@ energy_balance_metadata = PlaceholderMetadata(
 
     # Quality
     minimum_data_requirements="Mindestens 4 Tage mit Kalorienerfassung in 7-Tage-Fenster. Aktuelles Gewicht aus weight_log erforderlich.",
-    quality_filter_policy="Unvollständige Intake-Daten und fehlende Gewichtsmessung reduzieren Verlässlichkeit. TDEE-Schätzung ist vereinfacht (weight_kg × 32.5).",
+    quality_filter_policy=(
+        "Unvollständige Intake-Daten und fehlende Gewichtsmessung reduzieren Verlässlichkeit. "
+        "TDEE: Mifflin–St Jeor × PAL 1.55 wenn Höhe, Geschlecht, DOB und Gewicht vorhanden, sonst kg×32.5."
+    ),
     confidence_logic=(
         "Kombiniert Intake-Abdeckung und Robustheit des Verbrauchsmodells. "
         "Niedrigere Confidence bei <7 Tagen Daten oder fehlendem Gewicht. "
-        "TDEE-Modell ist vereinfacht → inherent uncertainty."
+        "PAL=1.55 ist ein Festwert (moderate Aktivität), kein individuelles Aktivitätslogging."
     ),
     missing_value_policy=MissingValuePolicy(
         available=False,
@@ -140,11 +143,10 @@ energy_balance_metadata = PlaceholderMetadata(
         legacy_display="nicht verfügbar"
     ),
     known_limitations=(
-        "TDEE-MODELL: Vereinfacht als bodyweight_kg × 32.5 (mittlerer Multiplikator). "
+        "TDEE: Bei vollständigem Profil (Größe, Geschlecht, DOB, Gewicht) Mifflin–St Jeor BMR × 1.55; "
-        "NICHT berücksichtigt: Aktivitätslevel, Alter, Geschlecht, Stoffwechselanpassungen. "
+        "sonst Fallback kg×32.5. PAL ist nicht nutzerkonfigurierbar. "
-        "TODO in Code: Harris-Benedict oder Mifflin-St Jeor für präzisere TDEE-Schätzung. "
+        "Energiebilanz ist modellbasiert, nicht gemessen. "
-        "ACHTUNG: Energiebilanz ist modellbasiert, nicht direkt gemessen. "
+        "Einheit kcal/Tag (Tagesmittel), nicht 7-Tage-Summe."
-        "Einheit ist kcal/Tag (daily average), NICHT 7d-Total."
     ),
 
     # Architecture

backend/placeholder_registrations/nutrition_score.py

@@ -60,8 +60,9 @@ nutrition_score_metadata = PlaceholderMetadata(
     ),
     known_limitations=(
         "Abhängig von user_focus_area_weights; ohne Ernährungs-Fokus liefert die "
-        "Funktion None. Kalorien-Adhärenz nutzt vereinfachte Heuristik (goal_type-TODO). "
+        "Funktion None. Kalorien-Adhärenz nutzt 7d-Energiebilanz vs. profiles.goal_mode "
-        "_score_macro_balance nutzt noch zeilenbasierte 28d-Abfrage (langfristig an "
+        "(weight_loss / strength+recomposition / sonst maintenance). "
+        "_score_macro_balance nutzt zeilenbasierte 28d-Abfrage (langfristig an "
         "Tagesaggregation angleichen)."
     ),
     layer_1_decision="Data Layer (nutrition_metrics.calculate_nutrition_score)",

docs/PLACEHOLDER_GOVERNANCE.md

@@ -18,7 +18,7 @@ This document establishes **mandatory governance rules** for placeholder managem
 ## 2. Scope
 
 These guidelines apply to:
-- All 116 existing placeholders
+- All 114 existing placeholders (canonical: `PLACEHOLDER_MAP`)
 - All new placeholders
 - All modifications to existing placeholders
 - All placeholder deprecations

docs/PLACEHOLDER_METADATA_DEPLOYMENT_GUIDE.md

@@ -79,7 +79,7 @@ curl -s -H "X-Auth-Token: $TOKEN" \
 **Expected response:**
 ```json
 {
-  "total_placeholders": 116,
+  "total_placeholders": 114,
   "available": 98,
   "missing": 18,
   "by_type": {

docs/PLACEHOLDER_METADATA_IMPLEMENTATION_SUMMARY.md

@@ -9,12 +9,12 @@
 
 ## Executive Summary
 
-This document summarizes the complete implementation of the normative placeholder metadata system for Mitai Jinkendo. The system provides a comprehensive, standardized framework for managing, documenting, and validating all 116 placeholders in the system.
+This document summarizes the complete implementation of the normative placeholder metadata system for Mitai Jinkendo. The system provides a comprehensive, standardized framework for managing, documenting, and validating all 114 placeholders in the system.
 
 **Key Achievements:**
 - ✅ Complete metadata schema (normative compliant)
 - ✅ Automatic metadata extraction
-- ✅ Manual curation for 116 placeholders
+- ✅ Manual curation for 114 placeholders
 - ✅ Extended export API (non-breaking)
 - ✅ Catalog generator (4 documentation files)
 - ✅ Validation & testing framework
@@ -75,7 +75,7 @@ This document summarizes the complete implementation of the normative placeholde
 
 ### 1.3 Complete Metadata Definitions
 
-#### `backend/placeholder_metadata_complete.py` (220 lines, expandable to all 116)
+#### `backend/placeholder_metadata_complete.py` (220 lines, expandable to all 114)
 
 **Purpose:** Manually curated, authoritative metadata for all placeholders
 
@@ -106,7 +106,7 @@ PlaceholderMetadata(
 
 **Key Features:**
 - Hand-curated for accuracy
-- Complete for all 116 placeholders
+- Complete for all 114 placeholders
 - Serves as authoritative source
 - Normative compliant
 
@@ -285,7 +285,7 @@ pytest backend/tests/test_placeholder_metadata.py -v
                       v
 ┌─────────────────────────────────────────────────────────────┐
 │                   Complete Registry                          │
-│  (116 placeholders with full metadata)                      │
+│  (114 placeholders with full metadata)                      │
 └──────────┬──────────────────────────────────────────────────┘
            │
            ├──> Generation Scripts (generate_*.py)
@@ -309,7 +309,7 @@ pytest backend/tests/test_placeholder_metadata.py -v
 ### 3.1 Metadata Extraction Flow
 
 ```
-1. PLACEHOLDER_MAP (116 entries)
+1. PLACEHOLDER_MAP (114 entries)
    └─> extract_resolver_name()
    └─> analyze_data_layer_usage()
    └─> infer_type/time_window/output_type()
@@ -468,7 +468,7 @@ curl -H "X-Auth-Token: <token>" \
 
 # Output:
 {
-  "total_placeholders": 116,
+  "total_placeholders": 114,
   "available": 98,
   "missing": 18,
   "by_type": {
@@ -599,7 +599,7 @@ The system is designed for extensibility:
 ## 8. Compliance Checklist
 
 ✅ **Normative Standard Compliance:**
-- All 116 placeholders inventoried
+- All 114 placeholders inventoried
 - Complete metadata schema implemented
 - Validation framework in place
 - Non-breaking export API