Error patterns in age estimation of harp seals (Pagophilus groenlandicus): results from a transatlantic, image-based, blind-reading experiment using known-age teeth
Bibliographic record
Abstract
Abstract Frie, A. K., Fagerheim, K-A., Hammill, M. O., Kapel, F. O., Lockyer, C., Stenson, G. B., Rosing-Asvid, A., and Svetochev, V. 2011. Error patterns in age estimation of harp seals (Pagophilus groenlandicus): results from a transatlantic, image-based, blind-reading experiment using known-age teeth. – ICES Journal of Marine Science, 68: 1942–1953. Blind readings of known-age samples are the ultimate quality control method for age estimates based on hard tissues. Unfortunately, this is often not feasible for many species because of the scarcity of known-age samples. Based on a unique collection of known-age teeth of harp seals (age range: 1–18 years), ageing errors were evaluated in relation to true age, reader experience, sex, and tooth format (images vs. originals). Bias was estimated by linear models fitted to deviations from true age, and precision was estimated as their residual standard error. Image-based blind readings of 98 tooth sections by 14 readers, representing different levels of experience, generally showed high accuracy and precision up to a seal age of ∼8 years, followed by an increasingly negative bias and increased variance. Separate analyses were therefore conducted for young seals (1–7 years) and older seals. For young seals, moderate associations were found between reader experience and levels of bias, precision, and proportions of correct readings. For older seals, only precision levels showed a significant association with reader experience. Minor effects of sex and tooth format are unlikely to affect these main patterns. Observed errors, even for highly experienced readers, may affect important age-related parameters, emphasizing the importance of known-age calibration of the output from all readers.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".