Broad-scale resource selection and food habits of a recently reintroduced elk population in Missouri
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since being extirpated from eastern North America, elk (Cervus elaphus) have been reintroduced in 10 eastern states and 1 Canadian province. However, little is known about the habitat needs of eastern elk populations. Our objectives were to determine broad-scale resource selection and food habits of the recently reintroduced elk population in Missouri. To achieve these objectives, we placed GPS collars on all adult animals prior to their release. To determine elk resource selection, we defined nine resource attributes using GIS layers. We modeled resource selection using a hierarchical Bayesian discrete choice model. Elk selection for forage openings (fields cultivated to provide forage for wildlife) was overwhelmingly greater than for all other landscape features. Elk also selected other attributes associated with open lands including glades, pastures, and low canopy cover. We determined seasonal diet selection of elk in Missouri by comparing use (diet composition) with forage availability. We measured diet composition through the microhistological analysis of feces. We determined forage availability through vegetation sampling at stratified random points. Elk selected grains and cool-season grasses over all other forage classes. Legumes were the most highly consumed forage class by elk. Approximately half of the elk diet was composed of plants cultivated in forage openings. The availability of open lands is a critical resource for elk in forest dominated landscapes. Managers of elk in similar ecosystems should ensure the availability of open lands is sufficient.
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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) | 0.000 | 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 it