Linking time budgets to habitat quality suggests that beavers (<i>Castor</i> <i>canadensis</i>) are energy maximizers
Bibliographic record
Abstract
According to optimal foraging theory, consumers make choices that maximize their net energy intake per unit of time. We used foraging theory as a framework to understand the foraging behaviour of North American beavers (Castor canadensis Kuhl, 1820), an important herbivore that engineers new habitats. We tested the hypothesis that beavers are energy maximizers by verifying the prediction that they allocate time to foraging activities independently of habitat quality in Kouchibouguac National Park of Canada in New Brunswick, where nearly five decades of unabated colonization by beavers led to family units established in habitats of varying quality. We observed the behaviour of 27 beavers at seven ponds from May to August 2001, at dusk and dawn. Habitat quality did not influence time that beavers allocated to foraging. This finding supported our hypothesis. The only factor in the best model explaining time spent foraging was the progression of spring and summer seasons (weekly periods). Limiting factors such as infrastructure maintenance and intermittent reactions to danger remain poorly understood for this important herbivore. Future research should focus on establishing the importance that habitat quality (food availability) and environmental stress (weather, predators) have on shaping its time budget and, consequently, its survival and reproductive success.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".