Estimated effects of clear-cuts and burns associated with habitat use by female Newfoundland Caribou (<i>Rangifer tarandus</i>)
Bibliographic record
Abstract
The decline of Caribou (Rangifer tarandus) is mainly attributed to anthropogenic disturbance from resource development (i.e., logging, oil and gas extraction), which causes habitat loss and increased predation risk. Natural landscape disturbance, particularly from fire, can have similar effects, and cumulative effects from disturbance have been associated with lower neonate recruitment. Our objective was to evaluate the potential effects of land cover types on resource selection by females, with an emphasis on clear-cuts and fire, during the calving season (May–June) in three neighbouring herds (Middle Ridge, Gaff Topsails, and Pot Hill) on insular Newfoundland, Canada, and compare results with pre-existing information on calf recruitment. We applied a resource selection framework to analyze location data collected from global positioning system collars between 2007–2010 and estimate relative probability of use for different cover types. Recruitment was lowest in Pot Hill, where ≤10-year old clear-cuts were favoured, whereas recruitment was highest in Middle Ridge and Gaff Topsails, where females favoured burns, suggesting that burns could be more beneficial to Caribou fitness. Further investigation will be needed to more closely examine how anthropogenic versus natural disturbance affects Caribou fitness in Newfoundland and improve our understanding of important habitat for calving females.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".