Use of residual strips of timber by moose within clearcuts in Northwestern Ontario / by Brian G. Mastenbrook
Bibliographic record
Abstract
Leaving strips of uncut timber within clearcuts has been \nquestioned as an effective option for moose (Alces alces) \nmanagement. Winter use of strips of timber was examined in six \nstudy sites in Northwestern Ontario. Winter aerial track surveys \nand spring browse surveys in 1987 and 1988 showed that moose used \nareas near the strips of residual timber within clearcuts during \nthe winter. The area within 45 m of the strips was preferred \n(p<0.05) in 2 of 11 cases and used as available in the remaining \n9 cases. The area within 90 m of the strips was preferred in 5 \nof 11 cases and used as available in the other 6 cases. Aerial \ntrack survey data also showed that moose significantly (p<0.01) \npreferred the area within 45 and 90 m of cover. Analysis of \nspring browse survey data showed no significant (p<0.01) \ndifference between the number of stems available or browsed that \nwas related to distance from the strips. Significant (p<0.01) \ndifferences between the number of twigs available and browsed \nwere found but differences in browsing seemed related to \navailability rather than increasing distance from the strips. \nSnow surveys showed significantly (p<0.01) lower snow depths \nwithin the strips than in the cutover. Snow depth and conditions \nadjacent to the corridor may have been influenced by the strips, \nbut were also influenced by wind, terrain and ground cover. \nResidual strips of timber were not being used specifically for \nfeeding areas but may have been used as escape cover, thermal \ncover or as travelling areas.
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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 | 0.001 | 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) | 0.002 | 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".