Winter use of upland conifer alternate strip cuts and clearcuts by moose in the Thunder Bay District / by Charles J. W. Todesco
Bibliographic record
Abstract
Moose (Alces alces) utilization o? five paired strip cut - \nclearcut areas was studied during the winters of 1983 - 84 and \n1984 - 85. Winter aerial reconnaissance flight data were \nsupplemented by snow condition observations and spring browse and \npellet group data. Greater (P < 0.05) numbers of moose were \nlocated in the clearcuts than the strip cuts in the first winter, \nand approximately equal numbers of moose were observed in both the \nfollowing winter (non significant). Clearcuts had significantly \n(P < 0.05) more track aggregates and area covered by tracks during \nboth winters. Forage production (kg/ha) and browse stem densities \nwere significantly (P < 0.05) higher in the clearcuts. No \nsignificant correlations occurred between browse production or \nbrowse availability and observed utilization levels in the strip \ncuts or clearcuts. In the strip cuts, moose preferred the open \nharvested strips and 94% of all moose observed in the strip cuts \nwere cows with calves or single cows. Moose preferred the 30 m \ninfluence zone edge habitat in the clearcuts, and adult bulls were \nthe most often observed moose in the clearcuts (38% of all moose \nsighted). Wolf tracks were observed in both types of timber \nharvest, ranging freely across the clearcuts and only on road \nsystems or waterways in the strip cuts. Snow conditions in the \nstrip cuts appear to inhibit wolf movements throughout these \nareas; however, they may preclude the use of strip cuts by moose \nin heavy snowfall winters. Alternate strip cuts provide suitable \nwinter habitat for moose, particularly for the reproductive social \ngroups. Clearcuts are not avoided by moose in the winter months, \nalthough seasonal utilization of individual habitats within the \nclearcuts does occur.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".