Sexing Arctic Terns in the Field and Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined sexual size dimorphism of Arctic Terns (Sterna paradisaea) from a breeding colony in northeastern North America. Each bird was sexed using DNA extracted from feather pulp. Body morphometrics recorded included mass, natural wing chord, head-bill, tail fork, culmen, depth of bill at the gonys, and tarsus. Two discriminant functions identified head-bill and bill depth as the best measurements to identify the sexes. The first function included head-bill only and correctly classified 73% of our sample. The second function included both head-bill and bill depth, correctly classified 74% of our sample and increased the ability to correctly sex individuals within a pair. We provide a method for researchers to calculate the probability of sexing Arctic Terns. This is done by fitting a non-linear equation through a plot of the probability of classifying an individual and the discriminant scores. Male Arctic Terns were generally larger in head-bill and bill depth than female Arctic Terns; however, we did not find evidence for assortative mating. With some species, morphometrics alone can be used to distinguish the sexes but for species such as Arctic Terns, which have a high degree of overlap between the sexes, it is recommended that a combination of morphometrics and genetic analysis is used to obtain the highest accuracy in sexing individuals correctly. Comparison of the morphometrics of northeastern North American and British populations of Arctic Terns suggests that these discriminant functions can be applied to both.
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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) | 0.000 | 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 it