/**
 * @copyright Sister Software
 * @license AGPL-3.0
 * @author Teffen Ellis, et al.
 *
 *   Opt-in confidence calibration for decoded spans.
 *
 *   The decoder emits a per-span `confidence` that is the mean of the span's per-token softmax
 *   probabilities (`build-tree.ts`). Softmax probabilities are NOT calibrated — a CE-trained model
 *   is systematically over/under-confident in bands. Task #59 fits an isotonic-regression
 *   calibrator on a held-out OpenAddresses + corpus set
 *   (`scripts/eval/fit-isotonic-calibration.py`) and ships the result as a 20-bin lookup table
 *   (`data/eval/calibration/isotonic-<locale>-<version>.json`).
 *
 *   This module turns that table into a pure `(rawConfidence) => calibratedConfidence` function. It
 *   is deliberately decoupled from the table source: pass the PARSED JSON object so this stays
 *   browser-safe (no `node:fs`) — the demo imports the JSON directly, Node scripts `JSON.parse`
 *   it.
 *
 *   Wiring is OPT-IN. The default decode path is unchanged (byte-stable `conf=` output). A caller
 *   that wants calibrated confidences builds a `Calibrator` here and passes it via
 *   `ParseOpts.calibrate` (neural) / `BuildTreeOpts.calibrate` (decoder), which `build-tree.ts`
 *   applies in `flush()`.
 */
/**
 * One row of the lookup table: a confidence bin and the calibrated value at its center.
 */
export interface CalibrationBin {
    lo: number;
    hi: number;
    center: number;
    calibrated: number;
}
/**
 * The full calibration artifact emitted by `fit-isotonic-calibration.py`.
 */
export interface CalibrationTable {
    model: string;
    model_version: string;
    method: string;
    bins: number;
    table: CalibrationBin[];
    [key: string]: unknown;
}
/**
 * Maps a raw span confidence in [0, 1] to its calibrated probability of correctness.
 */
export type Calibrator = (rawConfidence: number) => number;
/**
 * Build a calibrator from an isotonic lookup table. The mapping is piecewise-linear between bin centers and clamped to
 * the table's range outside it (the table is monotone non-decreasing by construction, so the interpolation is monotone
 * too). Accepts either the full `CalibrationTable` or a bare `CalibrationBin[]`.
 */
export declare function createCalibrator(table: CalibrationTable | CalibrationBin[]): Calibrator;
/**
 * Clamp a confidence into `[0, 1]`, mapping NaN to 0.
 *
 * Distinct from `clampFraction` (`@mailwoman/spatial`), which lets NaN through on purpose: a confidence that cannot be
 * computed is no confidence, while an interpolation fraction that cannot be computed must stay detectable rather than
 * silently snapping to a segment's start.
 */
export declare function clampConfidence(v: number): number;
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