Moose declines in Ontario, Canada: Mixed messages easily equate to mismanagement
Bibliographic record
Abstract
In 2019, interest groups and the responsible government agency in Ontario, Canada conducted a public review, soliciting opinions on how to manage moose. The review resulted in administrative changes to the program but failed to prevent a further decline in the Ontario moose population and to achieve any increase in moose harvest or hunter numbers. There are several probable reasons for these failures: an information system that did not present management information in a manner that facilitated effective interpretation; a lack of understanding of tools that had been in place for 35 years; a questionnaire that was misleading and did not present clear and practical solutions; and a dependence on public opinion over scientific evidence. Neither the lead advisory group to the government agency nor the Ontario government has a mandate to advise on the First Nations and Métis moose hunt, so Indigenous groups were not targeted to participate in the public review. There were three main themes in the information provided to the Ontario public. The first was that moose calves were being overharvested, the second was that by killing calves, hunting opportunities for adult moose were being reduced, and the third was a need to reduce hunter tag fill rates to create more hunting opportunities. As a result of the review, Ontario embarked on a radical change to its moose harvest management strategy that increased the harvest of adult females (cows) and reduced the harvest of calves. The presumed evidence for the cow-for-calf harvest exchange strategy was a local initiative near the southern edge of the moose range in Ontario with ambiguous outcomes, misrepresented as an “experimental area.” Some positive changes were adopted: a quota on the calf harvest, a legal framework that could permit direct and predictable control on the harvest, and a points-based tag distribution system.
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 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".