Preliminary investigation of the effects of timber harvesting on the activity status of beaver lodges in central Ontario, Canada
Bibliographic record
Abstract
Beavers (Castor canadensis) are widely considered a keystone species in boreal and northern temperate forest ecosystems and are seasonally dependent on intolerant hardwood tree species for food. We used existing data to investigate the effects of timber harvesting on the activity status of beaver lodges in central Ontario, Canada. Beaver lodges were initially visited from 1976 to 1979 and active lodges were revisited in 1994. We analyzed a sample of 100 lodges. Fifty had some timber harvesting within 400 m of the lodge, 41 of which had harvest within 100 m of the shoreline (subsequently referred to as shoreline harvest). We differentiated timber harvest by type (clearcut vs. partial cut), years since harvest, and location for each lodge. Clearcut timber harvesting appeared to have a positive effect on the occupancy of beaver lodges. Seventy-three percent of lodges adjacent to shoreline clearcut areas were active, whereas only 34% of lodges with no shoreline harvest were active. We developed logistic regression models to evaluate the relationship between presence of timber harvest and activity status based on harvest location, harvest type, years since harvest, and water feature (pond, stream, lake, or wetland). Using Akaike’s Information Criterion, our modeling suggested that the presence of a 21- to 35-year-old shoreline clearcut adjacent to a lodge, combined with the associated water feature type, was the best predictor of lodge activity. However, our dataset included only a small sample of lodges in this harvest category (n = 11). While these results are viewed as preliminary, they do suggest that further investigation into the effects of shoreline timber harvest and shoreline reserves on habitat suitability for beavers is warranted. Key words: beaver, beaver pond, boreal, clearcut, forestry, Great Lakes – St. Lawrence, natural disturbance, Ontario, partial harvest, riparian, shoreline
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".