Lek habitat suitability for the sharp-tailed grouse (Tympanuchus phasianellus jamesi) on the Northern Great Plains
Bibliographic record
Abstract
Grassland birds in North America face many problems as a result of habitat loss and fragmentation; understanding their habitat requirements is critical for their conservation and management. The sharp-tailed grouse (Tympanuchus phasianellus) can be found throughout North American grasslands and is a species of economic and cultural importance, but it has experienced population declines over the last few decades. A large part of sharp-tailed grouse life history is focused on and around lekking grounds, which makes leks an essential feature for sharp-tailed grouse management. We used information from 596 leks and landcover predictors within 1-km and 5-km squares to perform Habitat Suitability Index modeling for sharp-tailed grouse on the Northern Great Plains in Saskatchewan, Canada. The proportion of grasslands at the 5-km scale and the 1-km scale were the two most important factors affecting lek occurrence (permutation importance = 34.8% and 26.9%, respectively). In every case, the 5-km scale predictors were ranked as having a more significant influence on lek occurrence than the 1-km scale. Other factors of importance included topographic roughness (9.7% permutation importance), and the proportion of human disturbance at the 5-km scale (5% permutation importance). Our study highlights the importance of large patches of grassland to support the occurrence of sharp-tailed grouse leks, and that a diverse set of habitat features are needed for sharp-tailed grouse management.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".