Effects of industrial disturbance on abundance and activity of small mammals
Bibliographic record
Abstract
Anthropogenic disturbance can negatively impact animal populations and alter the behaviour of individuals. Disturbance associated with the energy sector has been increasing in the boreal forest of northern Alberta. Disturbances associated with the oil and gas industry vary in the infrastructure present and sensory stimuli generated. Two common types are compressor stations and roads. It is important to assess population consequences of disturbance on small mammals because they serve as prey, predators, and seed–spore dispersers in the terrestrial ecosystems that they inhabit. To test the effects of disturbance from the energy sector on the abundance and activity of small mammals, we used mark–recapture methods and live-trapped in forested areas with one side adjacent to a clearing with industrial infrastructure present (road or compressor station) or absent (control sites). We found no difference in abundance or activity of deer mice (Peromyscus maniculatus (Wagner, 1845)) and southern red-backed voles (Myodes gapperi (Vigors, 1830)) between sites and did not detect an edge effect on abundance within sites, regardless of the presence of industrial infrastructure. Our results suggest minimal effects of industrial disturbance on the abundance and activity of these species, and the infrastructure and sensory stimuli generated are unlikely to be key drivers of their population dynamics or behaviour.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".