Roads, logging, and the large-mammal community of an eastern Canadian boreal forest
Bibliographic record
Abstract
We evaluated hypotheses concerning the distributions of large mammals in a 60 000 km2study area that encompassed the contact zone between Ontario’s roadless north and the postlogging southern landscape. We estimated occurrence probability in 575 sample units for woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)), wolverine ( Gulo gulo (L., 1758)), gray wolf ( Canis lupus L., 1758), moose ( Alces alces (L., 1758)), and white-tailed deer ( Odocoileus virginianus (Zimmerman, 1780)). We used ordinations and spatial regressions to assess the contributions of parameters to species occurrence. Roads and cutovers were most abundant in the south, leading to an increased prevalence of deciduous forest. Mature coniferous forest, however, occurred most commonly in the north. Occurrence probabilities for moose and deer were greatest in the south, in close association with deciduous trees. Wolf occurrence was also greatest in the south, positively related to both deciduous forest and road density. Caribou occurrence, however, was positively related to mature coniferous forest and negatively related to both wolf occurrence and roads. Wolverine occurrence was negatively related to deciduous forest. Our surveys demonstrated distinct mammal communities in the northern and southern halves of our study area, a separation that appeared to be mediated by deciduous forest and roads.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".