from dataclasses import dataclass
from typing import Dict, Any, List

@dataclass
class ScoreResult:
    score: float
    details: Dict[str, Any]

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 700

    has_corr = "correlation" in p or "convergence" in p
    has_div = "diversity" in p or "response" in p
    has_trend = "decline" in p or "trend" in p
    has_time = "onset" in p or "year" in p
    has_tip = "tipping" in p or "proximity" in p

    raw = (
        0.15 * int(words_ok) +
        0.25 * int(has_corr) +
        0.20 * int(has_div) +
        0.20 * int(has_trend) +
        0.10 * int(has_time) +
        0.10 * int(has_tip)
    )
    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id")})

def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
    if not results:
        return {"mean": 0.0, "n": 0}
    return {"mean": sum(r.score for r in results)/len(results), "n": len(results)}