Resource Selection by Sheep and Goats in Queensland Australia
Bibliographic record
Abstract
Abiotic and biotic factors influence sheep and goat landscape distribution. Resource selection functions allow us to determine which factors influence distribution the most. This study tested distance to water, distance to trees, tree count, wind direction, and vegetation metrics as resources influencing the distribution of sheep and goats on extensive pastures in Queensland, Australia. Vegetation metrics were computed from remotely sensed data, and are measured as green vegetation, nongreen vegetation, bare ground, and total standing dry matter. We found the location of water, trees, and the prevailing wind direction were the most influential factors affecting sheep and goat distribution. Both sheep and goats selected for areas close to water, trees, and in the direction of the prevailing wind. At one site, goats showed a preference for green vegetation mainly in treed areas, while sheep showed a preference for nongreen vegetation which in the drought conditions of this study were found in less treed areas and can be interpreted as a selection for herbaceous material. At the other sites, the influence of water, trees, and wind was too strong to see a direct influence of vegetation on resource selection. Further research in nondrought conditions would help to better explain vegetation influence on sheep and goat landscape utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".