Reliability of External Characteristics to Age Barrow's Goldeneye
Bibliographic record
Abstract
ABSTRACT Accurate assignment of age class is critical for understanding most demographic processes. For waterfowl, most techniques for determining age class require birds in hand, reducing utility for quickly and efficiently sampling a large portion of the population. As an alternative, we sought to establish an observation‐based methodology, achievable in the field with standard optics, for determining age class of Barrow's goldeneyes ( Bucephala islandica ). We photographed heads, wings, and bellies of 232 Barrow's goldeneyes captured during late winter (February–April) of 2007–2015 along the north Pacific Coast. From these photographs, we focused on 5 external characteristics for both males and females, with binary states that putatively corresponded to 2 age classes—first‐year birds (<1 yr) and adults (>1 yr). For males, all 5 external traits (belly color, head color, eye color, facial crescent, median secondary coverts color) had binary states that were reliably distinguishable by observers. Moreover, all 5 external traits were highly predictive of age class (≥96% concordance between external vs. bursal‐derived ages), and novice observers, after receiving training, were able to accurately age 96% of first‐year and 99% of adult males. In contrast, patterns were weaker for females; putative external characteristics of female age class (belly color, bill radiance, bill blackness, eye color, median secondary coverts color) had 77–91% concordance with bursal‐derived age, compared with 96–100% for males, and observers misidentified age classes of 15% of females, compared with only 2% of males. Overall, age classes of male Barrow's goldeneyes were accurately and reliably distinguishable during winter based on several external characteristics, whereas those of females were not. Our technique may be used to estimate age composition of male Barrow's goldeneyes during winter, providing a useful metric for monitoring annual changes in adult‐to‐juvenile ratios and other important demographic parameters. © 2020 The Wildlife Society.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".