Habitat selection of the Eurasian beaver (Castor fiber) near its carrying capacity: an example from Norway
Bibliographic record
Abstract
The Eurasian beaver ( Castor fiber L., 1758) was extirpated until the beginning of the twentieth century, but is becoming re-established over much of its former range. Since this rodent is considered both a keystone species and an ecosystem engineer, it is important to understand its habitat use in a population near its carrying capacity. In this study, we tested the hypothesis that habitat selection in beaver populations near their carrying capacities is different from populations that are still expanding. Also, a resource-selection model for the species using logistic regression was derived. Although most of the tested habitat variables were important for both populations expanding and populations near their carrying capacities, there were significant differences in the importance of the presence of shrub and (or) hardwood on the riverbank and the river flow speed. Beaver presence was associated with narrower river widths, bank slope, water depth close to the bank, silt–soil bank substrates, cover of deciduous forest, and cover of shrub and (or) hardwood on the bank. The models had high overall accuracy (overall correct classification >85%, area under the curve >90%). The final model is thought to be robust and applicable to areas with similar river and vegetation characteristics. Our results extend the habitat selection patterns of beavers to patterns at high-density populations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".