Effects of forest management, weather, and landscape pattern on furbearer harvests at large-scales
Bibliographic record
Abstract
Over the past 50 years, Ontario?s forest landscape has changed due to ever increasing \nnatural resource management. The natural vegetation pattern, forest composition, and the \nfire regime have been altered. Maintaining wildlife species diversity is an important goal \nof current forest management. However, little is understood about the impacts of large-scale \nland use and landscape scale processes that influence wildlife. This project used \ntrapline harvest statistics from 1972-1990 to identify broad-scale effects of forest \nmanagement, weather, and landscape structure on furbearers (marten, beaver, fisher, and \nlynx). \nSpatial variables for logging and fire disturbance, forest cover type, weather, spatial \npattern, and road density were compiled in a geographic information system (GIS) and \nstandardized by trapline. Regression models were created for each species and analysed \nat five spatial scales ranging from the Ontario Ministry of Natural Resources (OMNR) \ndistrict (5000 sq. km) to the ?provincial? (800,000 sq. km) scales. The models were then \ncompared temporally and spatially for consistency in variable contribution to the \nregression models. Forest cover type, weather, and spatial pattern variables accounted for \nthe greatest variation in furbearer harvest, while disturbance and road density variables \naccounted for little variation. Model predictive capability ranged from 10 to 55% for all \nspecies. Marten models had the greatest predictive power (r2) at the ?OMNR District? \nscale, while fisher and beaver models had the highest r2 values at the ?Hills site region? \nand ?provincial? scales, respectively. Lynx models were inconsistent with relatively low \npredictive power at all scales. \nThe models suggest that disturbance from forest management is not affecting furbearer \nharvests. Landscape scale variables such as forest cover type, weather, and landscape \npattern account for a relatively high proportion of marten, beaver, and fisher harvests. \nThese variables and the predictive power of the models reveal the influence that broad \nlandscape factors have on wildlife.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".