import type { HumanFeedback } from "../../runDirectory/humanFeedback.js";
import type { BaseGrader } from "./baseGrader.js";
import type { LoadedRun, Grade, Test } from "./types.js";
export type GraderGrade = {
    grader: BaseGrader;
    grade: Grade;
};
export type InputGrades = {
    test: Test;
    /** Null when the input was never graded — the run errored, or produced no output. */
    run: LoadedRun | null;
    grades: GraderGrade[];
    gatesPassed: boolean;
    /** Why this input scored 0 without being graded. Set iff `run` is null. */
    ungradedReason?: string;
    /** Notes and unchecked checklist questions people left on the trace, when
     *  the run directory carried any. Reflection feeds these to the model. */
    humanFeedback?: HumanFeedback;
};
/**
 * Weighted mean of every grade for one input: a scalar grade contributes its
 * value, a binary grade contributes 1.0 (pass) or 0.0 (fail). `mustPass` is an
 * orthogonal gate (a failed gate zeroes the whole input via the Scorecard); it
 * does not change whether a grade contributes here. An input with no grades
 * scores 0.
 */
export declare function inputObjective(grades: GraderGrade[]): number;
/** Per-candidate grading result: per-input grades plus derived gate/objective readouts. */
export declare class Scorecard {
    readonly perInput: InputGrades[];
    constructor(perInput: InputGrades[]);
    gatesPassed(): boolean;
    /** Per-input objective; a gate-failed input scores 0. */
    inputScores(): number[];
    objective(): number;
    /** The canonical gate-aware comparison score: the raw objective when every
     *  `mustPass` gate is satisfied, otherwise 0. This is what every optimizer
     *  uses to compare candidates, and what `OptimizeResult.trainObjective` and
     *  `baselineObjective` report. Prefer this over inlining
     *  `s.gatesPassed() ? s.objective() : 0`. */
    gatedObjective(): number;
}