Bibliographic record
Abstract
Chukar (Alectoris chukar) are a non-native bird that has naturalized in North America following its introduction over a century ago. Today, the species supports hunting opportunities in 11 U.S. states and one Canadian province. Despite its wide distribution and recreational value, key aspects of chukar ecology remain poorly understood. Limited information is available on the species' spatial ecology, including patterns of space use. Likewise, information regarding the influence of weather on population dynamics of chukar has not been formally conducted. As part of our investigation, we evaluated the space use of chukar in western Utah, USA utilizing GPS transmitters (Chapter 1). We found that home ranges occupied by chukar within our study were estimated to be larger than previous research conducted with VHF transmitters. Our findings demonstrated that chukar exhibit migratory behaviors, migrating both latitudinally and altitudinally. In addition to investigating the spatial ecology of chukar, we compiled a dataset across Utah, Idaho, and Nevada, USA to analyze the effects of weather on the population dynamics of chukar (Chapter 2). We found evidence that chukar densities were positively correlated to precipitation but negatively correlated to the average snow depth over a winter period. Chukar production was positively influenced by warmer springs, warmer winters, and wetter summers. Finally, the harvest of chukar within an area was positively correlated with temperatures from the winter preceding a hunting season. Overall, we estimate that chukar populations will be highest when precipitation is high due to increased food items and improved concealment from vegetation. Warmer winter and spring periods presumably had a positive influence due to lower metabolic requirements and less risk of embryos being damaged by cold temperatures. The relationship with chukar populations and snow was complex; negative influences may have arisen when periods of snow cover were extensive and inhibited access to food items but increases in snow that melts quickly could recharge soil moisture, leading to better nest and brood concealment as well as improved quality of food items.
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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 | 0.001 |
| 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) | 0.001 | 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".