Brown bear den characteristics and selection in eastern Transylvania, Romania
Bibliographic record
Abstract
Abstract Dens are important for species that need to survive and reproduce during harsh winters. Brown bears (Ursus arctos) in Romania, listed by the European Union as a population of concern, use dens for several months each year. To date, few quantitative assessments of denning habitat have been carried out for this population or others in Europe. In 2008–2013 and 2015–2017, we used local knowledge and telemetry data from brown bears fitted with GPS collars to identify 115 winter dens and eight open ground nests used by bears in eastern Transylvania, Romania. We located most dens in mountainous areas (64%) and fewer in foothills (36%). Den entrances in mountainous areas were significantly narrower than entrances in foothills, likely due to the need for reduced thermal loss during more severe winters at higher elevations. We selected seven habitat characteristics (abiotic and biotic) and human-related covariates associated with known locations of dens and open nests to identify potential brown bear denning habitat using maximum entropy modeling. We found that terrain ruggedness was the single most important factor when predicting bear denning habitat. The habitat map derived from this study can be used in the future to safeguard bear denning areas from potential human disturbances.
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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 | 0.001 |
| 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 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".