Comparing grazing and resting electivity of beef cattle for BC bunchgrass communities using GPS collars
Bibliographic record
Abstract
Thompson, D., Wheatley, B. J., Church, J. S., Newman, R. and Walker, J. 2015. Comparing grazing and resting electivity of beef cattle for BC bunchgrass communities using GPS collars. Can. J. Anim. Sci. 95: 499–507. Grasslands in the interior of British Columbia often contain a mosaic of plant communities that provide variable habitat for free-ranging cattle. Global positioning system (GPS) collars have been used to study natural habitat use by cattle on a coarse scale (such as riparian, grassland and forested habitats), but not on a fine scale (such as choice among grassland plant communities). Cows equipped with GPS collars were tracked during the spring grazing period for 4 yr. Six grassland pastures were used as replicates. The activity (grazing or resting) of cattle at GPS locations was classified using a distance travelled algorithm. A detailed plant community map of five plant community types was constructed, and cow relative use within the plant communities was determined. Electivity, which scales for differences in community area, was used to compare the use of plant communities. While grazing, electivity for the Kentucky bluegrass community (mean +0.3) was greater than for the bluebunch wheatgrass community (mean −0.2). While resting, these differences were more pronounced. GPS collars can be used to estimate fine-scale choices among grassland communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".