Martin et al_Wild turkey roost selection is more consistently associated with tree traits than microclimate
Bibliographic record
Abstract
We studied microclimate variables (specifically overnight wind speeds, air temperatures, and accumulated precipitation) and physical characteristics (diameter at breast height and whether the tree was coniferous or deciduous) at roost trees used by eastern wild turkeys (Meleagris gallopavo silvestris) near Peterborough, Ontario, Canada. We first collected location data from GPS-tagged eastern wild turkeys and used spatial analysis to identify roost trees. We then measured physical characteristics and collected microclimate data at both the roost trees and paired non-roost trees. We sampled 25 winter roost pairs and 30 summer roost trees (i.e., pairs in which the roost tree was known to be used by turkeys during winter and summer, respectively). For each pair, the non-roost tree was nearby (50 m away from the roost tree) and represented a tree that was available to turkeys but not apparently used (based on GPS-tagged turkeys and visual inspection for evidence of roosting). Data for winter roost trees were collected from December 1, 2022, to March 27, 2023; and data for summer roost trees were collected from June 1, 2023, to September 25, 2023. Finally, we used the dataset provided here to perform matched case-control conditional logistic regression models to assess how microclimate factors and tree characteristics influence roost tree selection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".