The influence of forestry stands treatments on brown bears (Ursus arctos) habitat selection in Sweden – an option for Alberta forestry?
Bibliographic record
Abstract
The brown bear population in Alberta, Canada has been decreasing, while the Swedish has been increasing and all the affecting parameters are not known. This study examined the difference in these populations to see if an explanation could be found in differences between the forest management. The aim of the paper was to see if stand treatments had an influence of brown bears habitat selection in autumn in Sweden and how the results can be used in Alberta. This was done by analyzing bear positions in Sweden with forest data, and comparing forest management data for the study area in Sweden with forest management data for Alberta. The results displayed that mature forests over 60 years that have been commercially thinned are selected by bears rather than forests over 60 years that have not been commercially thinned in. From pre-commercial thinning no conclusions for bears in general could be done, but males tend to select for stands that has been pre-commercial thinned. Forest management in the two study areas differs with the emphasis on pre-commercial thinning and commercial thinning being carried out in Sweden but not in Alberta. \n \nThe conclusion is that forest management influences bears habitat selection during autumn and the theory is that thinning increases the berry production by opening up the canopy and increasing the nutrient availability. Alberta might be able to promote their bear population by thinning, however experiments should be done to see if there is an increase in berry production in Alberta as in Sweden.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".