Sex Determination in Breeding Dunlin (<i>Calidris alpina hudsonia</i>)
Bibliographic record
Abstract
Male and female Dunlin (Calidris alpina) exhibit slight plumage and structural differences. Discriminant function analysis based on morphological characteristics can effectively differentiate between sexes in several subspecies of Dunlin. We assessed the level of sexual size dimorphism in a subspecies that breeds in sub-Arctic Canada (C. a. hudsonia), and used discriminant function analysis to create equations to classify individuals to sex using five body measurements (body mass, head length, culmen length, tarsus length, and flattened wing chord). Females were significantly larger than males for all body measurements. Discriminant function analysis using tarsus length, head length, and body mass correctly classified 87.1% of molecularly sexed females (n = 31) and 92.6% of males (n = 27). The classification of an independent sample (n = 12) resulted in 100.0% correct assignment of sex with 33.3% of individuals falling within the undetermined range. A discriminant function analysis equation is provided for use with non-breeding populations using only structural characteristics with classification accuracies of 83.9% for females and 81.5% for males. The resulting equations from this study have classification accuracies comparable to those equations developed for other Dunlin subspecies and can be used to reliably differentiate sexes of C. a. hudsonia using body measurements collected in the field.
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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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".