Wildlife winners and losers in an oil sands landscape
Bibliographic record
Abstract
Energy development and consumption drive changes in global climate, landscapes, and biodiversity. The oil sands of western Canada are an epicenter of oil production, creating landscapes without current or historical analogs. Science and policy often focus on pipelines and species‐at‐risk declines, but we hypothesized that differential responses to anthropogenic disturbances shift the entire mammal community. Analysis of data collected from 3 years of camera trapping and species distribution models indicated that anthropogenic features best explained the distributions of the ten mammal species included in the study. Relative abundances of some mammals were positively correlated with anthropogenic feature density, and others were negatively correlated. Effect sizes were often larger than for natural features. Increasing anthropogenic spatial complexity, access to multiple habitats, and new forage sources favor generalist predators and browsers, to the detriment of specialists, likely altering ecological processes. This issue has far‐reaching implications: as the oil sands landscape changes so too does its mammal community, serving as a bellwether of future change for energy landscapes worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".