Accuracy and precision of an umbilical‐based method for estimating birthdates of pre‐weaned harbour seal pups
Bibliographic record
Abstract
Abstract Fine‐scale age estimation of animals can provide insight into important biological processes but can be logistically difficult to measure in wild populations. We evaluated the accuracy and precision of an umbilical‐based method for fine‐scale age and birthdate estimation in a wild population of harbour seal pups ( Phoca vitulina ) in the St. Lawrence Estuary, Quebec, Canada. Our method consists of attributing umbilicus degeneration scores to estimate pup age in days. To assess the methods validity, we first constructed a score attribution test with field pictures of pup umbilical cords at various stages of degeneration. The score attribution test was completed by 8 observers, and we measured the accuracy and precision of the umbilicus degeneration score attribution by calculating Cohen's kappa as well as intraclass correlation coefficient (ICC). We used data from 758 pups (captured between 1998 and 2023) for which an umbilical degeneration score was assigned in the field to evaluate the efficiency of the score test to estimate birthdate. Our findings showed that observers can accurately and precisely attribute umbilicus scores, with a Cohen's kappa mean value of 0.93 which represent almost perfect agreement and an ICC value of 0.92 which is considered excellent precision of the method. Our results indicated that the umbilical‐based method can accurately estimate pup birthdates. We suggest the scoring method we developed provides a accurate approach to estimate age, when possible, to a previously used back‐calculation method based on body mass. Incorporating the measurement of umbilical score more broadly in research protocol has the potential to open the door to a wide range of ecological studies, including birth phenology, which are important to better understand population and ecosystem dynamics.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".