High-density Yellow Rail (Coturnicops noveboracensis) Population Beyond Purported Range Limits in the Northwest Territories, Canada
Bibliographic record
Abstract
The Yellow Rail (Coturnicops noveboracensis) is a secretive marsh bird of conservation concern in Canada. However, the status of this species in northern boreal regions remains largely unknown given uncertainty about population abundance and distribution. This knowledge gap is mainly due to limitations of traditional survey methods to detect this species. In this study, avian point count data collected from autonomous recording units and augmented by detections from a machine-learning recognizer were used to generate a species distribution model to provide habitat-specific density estimates and population size estimates for Yellow Rail breeding in the Edéhzhíe Dehcho Protected Area, Northwest Territories. This protected area is ∼150 km beyond the currently established northern range limit. A large population estimated at 906 (± 146) pairs was discovered. Yellow Rail were found at high densities in marshes (0.063 ± 0.004 males/ha), but were also observed in fens and bogs, albeit at much lower densities (0.003 ± 0.002 males/ha and < 0.001 ± 0.002 males/ha). Our results suggest both the range and the population size of Yellow Rail are much larger than currently reported. Further studies are required to provide better population size and distribution estimates to conserve this species at risk in Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".