Ground-truth standards, drift detection, and the human reference layer for computer vision and generative AI pipelines.
Every rating system presumes a reference. Follow the chain of calibration upward and you reach a fixed point — the rater the others are measured against. Most pipelines never define it. Then the drift starts.
Read: Every pipeline has a patient-zero problem →As evaluation automates, item-level judgment commoditizes. The authority that defines the standard — the gold set, the rubric, the severity taxonomy — diverges. RaterZero operates on the diverging line.
Gold sets, ground-truth dictionaries, rubric and severity-taxonomy design, edge-case adjudication. The standard your raters — and your auto-judges — are trained against.
Explore →A fixed-scope statistical audit of your annotation exports: PSI, KS, and Wasserstein across geometry, class, and shape distributions. A report — not a rate card.
Request →Measurement design for pipelines: what to measure, holdout hygiene, gold-set expiry, and validating the LLM judge before it validates anything else.
Explore →A named human reference for physical-ground-truth domains. Monthly calibration review and sign-off on ground-truth validity that survives an audit.
Explore →Why annotation drift spreads through rater populations that lack a fixed reference — and why the cure is a point, not a process.
How frozen references quietly expire while everything keeps calibrating against them.
Why stacking automated evaluators creates a regress that only a human anchor can terminate.
What happens to a model that is trained, measured, and approved entirely inside its own distribution.
Statistical drift scoring for COCO segmentation exports — PSI, KS, JS divergence, and Wasserstein-1 across area, vertex count, aspect ratio, and compactness.
● Open sourceA passive session mirror for annotators: dwell time, velocity, class distribution, and focus patterns. Self-reporting, never managerial.
● Open sourceAn independent calibration model trained on expert annotation data — an external referent for auditing production models from outside their loop.
● In training