Wildlife habitat selection on landscapes with industrial disturbance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY Technological advancements in remote sensing and telemetry provide opportunities for assessing the effects of expanding extractive industries on animal populations. Here, we illustrate the applicability of resource selection functions (RSFs) for modelling wildlife habitat selection on industrially-disturbed landscapes. We used grizzly bears ( Ursus arctos ) from a threatened population in Canada and surface mining as a case study. RSF predictions based on GPS radiocollared bears (n during mining = 7; n post mining = 9) showed that males and solitary females selected areas primarily outside mineral surface leases (MSLs) during active mining, and conversely inside MSLs after mine closure. However, females with cubs selected areas within compared to outside MSLs irrespective of mining activity. Individual variability was pronounced, although some environmental- and human-related variables were consistent across reproductive classes. For males and solitary females, regional-scale RSFs yielded comparable results to site-specific models, whereas for females with cubs, modelling the two scales produced divergent results. While mine reclamation may afford opportunities for bear persistence, managing public access will likely decrease the risk of human-caused bear mortality. RSFs are powerful tools that merit widespread use in quantitative and visual investigations of wildlife habitat selection on industrially-modified landscapes, using Geographic Information System layers that precisely characterize site-specific conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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 it