Error patterns in age estimation and tooth readability assignment of grey seals (Halichoerus grypus): results from a transatlantic, image-based, blind-reading study using known-age animals
Bibliographic record
Abstract
Abstract Frie, A. K., Hammill, M. O., Hauksson, E., Lind, Y., Lockyer, C., Stenman, O., and Svetocheva, O. 2013. Error patterns in age estimation and tooth readability assignment of grey seals (Halichoerus grypus): results from a transatlantic, image-based, blind-reading study using known-age animals – ICES Journal of Marine Science, 70: 418–430. We analysed error patterns in a first interlaboratory grey seal (Halichoerus grypus) age-reading experiment. The experiment involved ten readers, who estimated age using images of cementum growth layers from teeth of 68 known-age seals (0–22 years). The percentages of correct estimates ranged from 32.4% to 60.3% among readers, and 89.3% of all errors were by ±1–2 years. Six readers showed increasing underageing with increasing seal age. An elevated risk of underestimation by 1 year occurred in teeth collected 0–5 months after breeding and was attributed to more frequent absence of a distinct growth layer for the new year and lack of information on months between the last birthday and the date of sample collection (plusmonths). For plusmonths 6–11, positive bias was predominant, suggesting that overestimation is the more common error when plusmonth information is available. Readers assigned readability scores to the tooth sections, and 79.1% of all ageing errors occurred in sections of low or intermediate readability. Excluding these sections would, however, also exclude 43.0% of all correct estimates. Neither levels of age estimation error nor predictive values of readability assignments were associated with reader experience levels. Analyses of image markings identified common errors in delineations of annual increment layers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".