Sex discrimination and measurement bias in Northern Fulmars Fulmarus glacialis from the Canadian Arctic
Bibliographic record
Abstract
The Northern Fulmar Fulmarus glacialis is a seabird in which both sexes have similar plumage, but sexual dimorphism in body size is apparent. We used data from 63 Northern Fulmars collected at Cape Vera, Nunavut, Canada, to develop a discriminant function to predict sex that is based on key morphometric variables. The sex of Northern Fulmars from Cape Vera can be ascertained accurately using a combination of four body characters, and using either a site-specific or generalized discriminant function model. Some differences between variables entering in the model for Cape Vera compared to models developed elsewhere for North Atlantic Northern Fulmars may reflect local population differences in typical morphometry, notably in head and bill measures. Discriminant functions developed to identify male and female Northern Fulmars appear to be robust enough to accommodate bias introduced from measurements by different biologists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 teacher head, 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".