Golden-winged Warbler body fat and blood parasites are associated with anthropogenic and environmental habitat metrics
Bibliographic record
Abstract
The extent of early successional vegetation communities is declining worldwide. Sometimes species use early successional vegetation communities associated with anthropogenic development (e.g., rangeland, cropland, transportation corridors, aggregate mines), given that, at face value, these ecosystem types can share many of the same characteristics (e.g., lack of mature trees). This may have a negative impact on species’ health by lowering access to resources or by increasing exposure or susceptibility to vectors carrying disease agents leading to infections. We investigated whether proximity to anthropogenic development had a negative impact on several health metrics of a threatened, early successional species, the Golden-winged Warbler (Vermivora chrysoptera). Golden-winged Warblers with more rangeland/grassland within 200 m of their capture location were more likely to be infected with Haemoproteus parasites, whereas those with greater marsh cover within 200 m of their capture location were more likely to be infected with Leucocytozoon parasites, and those near rivers had higher body-fat scores. As infection prevalence and body-fat scores may have an impact on avian fitness, the variable response of these land-cover types indicates an additional conservation concern for this threatened species.
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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 | 0.001 |
| 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) | 0.002 | 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".