Lost in translation? Insights into caribou habitat selection from forest inventory data
Bibliographic record
Abstract
The gap between research and its implementation is an impediment to conservation of the environment. Translating science into actionable management and policy requires effective communication and collaboration among scientists, practitioners, and policy-makers. Ecologists routinely rely on spatial data to describe wildlife distributions; however, habitat definitions vary by species, and data sources often differ from those used by land managers. Finding commonalities in the language and data used to plan for industrial activities and wildlife conservation may help address the research-implementation gap for threatened species like woodland caribou. We built resource selection functions for caribou using Alberta Vegetation Index (AVI) habitat data, which is employed by the Alberta forest industry for landbase planning. Our goal was to bridge the research-implementation gap by providing the forest industry with tools to facilitate planning for caribou conservation within their jurisdiction. In contrast to previous studies that highlighted shortcomings in AVI data for predicting wildlife habitat use, we found that resource selection function models that combined AVI data with complementary covariates validated well to predict caribou habitat use. We suggest that by using a data source familiar to land managers, ecologists can facilitate the bridging of the research-implementation gap without compromising the quality of ecological modeling.
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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 | 0.081 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".