Eye Colour, Aging, and Decoy Trap Bias in Lesser Scaup, <em>Aythya affinis</em>
Bibliographic record
Abstract
Researchers routinely assume that samples of trapped or captured animals are representative of the overall population, though these assumptions are not always evaluated. We used decoy-trapped Lesser Scaup (Aythya affinis) to assess the reliability of classifying females as yearlings or adults from a distance, based on documented age-related eye-colour changes, and also to evaluate the presence of sex, condition and age biases in decoy trapping. We compared eye colour of trapped females to photographs of known-age females following a published procedure while females were (1) in traps (by using spotting scopes or binoculars) and (2) in-hand. Assuming in-hand age assessments were correct, we found that adults aged from a distance were frequently misclassified as yearlings, but yearlings were never misclassified as adults. Distance between observer and female, overall observation quality, and cloud cover did not influence age assignment success. A larger proportion of males was captured than observed during a survey of the local breeding population. We also found that decoy-trapped females had lower body mass and were more likely to be yearlings compared to pass- and jump-shot females from the same area. We conclude that female Lesser Scaup cannot be accurately aged from a distance using eye colour and concur with other researchers that possible sex, age and condition biases should be evaluated when using decoy traps.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".