martin et al_turkey roost microclimate data.xlsx
Bibliographic record
Abstract
We investigated microclimate (specifically, ambient temperature, wind speed, and precipitation) at roost sites used by wild turkeys (Meleagris gallopavo) in Ontario, Canada. We collected data from pairs of roost and non-roost trees. We sampled 25 and 30 pairs of trees in winter and summer, respectively. Roost trees were known to be used by turkeys within the time period of 2017–2019 and 2022 based on GPS location data from tagged turkeys and observation of evidence of use (e.g., droppings and feathers below the tree). Non-roost trees were nearby to roost trees, with each one 50 m away to its paired roost tree. Non-roost trees represented trees that were 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. In addition to the paired trees, we also collected microclimate data at a single non-forested site located within our study area. We used the same site during winter and summer, and the site consisted of a deciduous tree in a hedgerow surrounded by agricutural fields. Data were collected using Kestrel 5000 Environmental Meters mounted on a platform that hung from a branch on the tree, and using a pail on the ground for capturing precipitation. The excel file contains five sheets, with metadata and additional information available on the first sheet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.646 | 0.235 |
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".