Using morphometrics to sex adult and juvenile Soras ( Porzana carolina )
Bibliographic record
Abstract
Determining the sex and age of individuals can be an essential element of conservation management, wildlife monitoring, and demographic analysis. For many members of the family Rallidae, distinguishing between males and females is challenging, even when the bird is in the hand. The Sora (<em>Porzana carolina</em>), a secretive rail that occupies freshwater wetlands throughout the United States and Canada, represents a species that is challenging to sex in the field. Morphometric measurements can help sex birds of an array of species, including rails. However, no comprehensive morphometric model has been fully validated for sexing Soras. We used DNA analysis to confirm the sex of Soras captured in the field and logistic regression models to determine which morphological features were the best predictors of sex. Measurements from 108 Soras (31 hatch year females (HY-F), 29 hatch year males (HY-M), 22 after hatch year females (AHY-F), and 26 after hatch year males (AHY-M) were used to create our logistic regression model. Color definition and connectivity of the auricular patch to eye or nape was used as an additional characteristic in adult birds. Our top-ranked model was further validated using a sample of 72 individuals exhibiting intermediate traits that would be particularly challenging to distinguish in the field. Our top performing model incorporated culmen length and tarsometatarsus length as the features most predictive of sex and had an overall accuracy of 85%. If higher accuracy is desired, an inconclusive band, which eliminates birds of low model score, i.e., scores indicative of inconclusive sex (below + or - 1.2), can be used. The accuracy of remaining birds (75% of sample) will be increased to 95%. Our model shows that simple measurements of culmen and tarsometatarsus is useful in discriminating the sex of a large percentage of live-caught Soras. This morphometric model will facilitate further demographic studies of this species and may be useful in designing morphometric studies of other species in the family Rallidae.
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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) | 0.000 | 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 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".