Patterns of distribution, relative abundance, and microhabitat use of anurans in a boreal landscape influenced by fire and timber harvest
Bibliographic record
Abstract
Ecosystem management is a theoretical framework in which land managers attempt to approximate natural disturbance with harvesting practices. In the mixedwood boreal forest of northeastern Alberta, Alberta-Pacific Forest Industries Inc. alters cutblock size, structure, and distribution over the landscape to simulate fires, the dominant disturbance type. In 1997 and 1998, we sampled for Rana sylvatica (Le Conte) and Pseudacris triseriata maculata (Wied-Neuwied) near Owl River and Mariana Lake, Alberta, in undisturbed, harvested, and naturally burned landscapes. We compared patterns of distribution and relative abundance using transects, time-constrained lake margin searches, and opportunistic detections. In 1998, we characterized the understory, shrub layer, and canopy layer on each transect. We used stepwise logistic regression to describe microhabitat use by each species. We did not detect consistent differences between burned and logged areas. This may reflect pre-treatment variation in regional habitat. Our data suggest that the presence of R. sylvatica is related to deciduous leaf litter, and that both species may require extensive ground cover and moist soil conditions. Although the microhabitat descriptions we present can be used to plan future harvests, further work is required to determine the effectiveness of ecosystem management in the boreal forest.
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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 | 0.000 |
| 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) | 0.001 | 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".