Errors in identification using natural markings: rates, sources, and effects on capturerecapture estimates of abundance
Bibliographic record
Abstract
The results of a double-marking experiment using natural markings and microsatellite genetic markers to identify humpback whales (Megaptera novaeangliae) confirm that natural markings are a reliable means of identifying individuals on a large scale. Of 1410 instances of double tagging, there were 414 resightings. No false positive and 14 false negative errors were identified. The rate of error increased with decreasing photographic quality; no errors were observed among photographs of the highest quality rating, whereas an error rate of 0.125 was identified in sightings for which only part of the area used for identification was visible. There was also a weaker relationship between error rate and the distinctiveness of markings, which may result from non-independence in coding for image quality and distinctiveness. A correction is developed for the Petersen two-sample abundance estimator to account for false negative errors in identification, and a parametric bootstrap procedure for estimation of variance is also developed. In application to abundance estimates from the North Atlantic, the correction reduces the bias in estimates made using poorer quality photographs to a negligible level while maintaining comparable precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".