Understanding Fluctuations in Bobcat Harvest at the Northern Limit of Their Range
Bibliographic record
Abstract
ABSTRACT In Quebec, Canada, harvest of bobcats ( Lynx rufus ) started to decline in 1985 and by 1991, harvest seasons were closed due to concerns of a perceived population decline. Since the closing of harvest season in 1991, the average temperature has increased, snow quantity has decreased, and important changes in agriculture and forest management have occurred. In light of changing conditions, the situation of Quebec bobcats needed reassessment. Thus, we analyzed harvest data to clarify the current status of bobcat populations in Quebec. From 1980 to 1991, bobcat harvest in Quebec was strongly correlated with bobcat harvest in Maine (USA), Nova Scotia (Canada), Ontario (Canada), and Vermont (USA). Extrapolations of harvest in Quebec relative to harvest in Maine, Ontario, Vermont, and Nova Scotia suggested an increase in number of bobcats after 1991. Mass of male and female bobcats before 1991 was less than mass of animals captured after 1991. Percentage of juveniles in the reported harvest before 1991 was higher than after 1991. However, percentage of males and litter sizes in the harvest did not differ before and after 1991. The geographic distribution of bobcats captured has gradually expanded after the closure of the harvest season. Our findings suggest that bobcat populations in Quebec have recovered from the 1985–1991 decline, and that the harvest season for this species could resume. This study also illustrates how managers can rely on data from neighboring jurisdiction to manage species when local harvest data is unavailable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".