12. Black Bear Conservation efforts in Ontario
Bibliographic record
Abstract
Over the past 200 years, large carnivore range has been decreased due to an increase in human density and influence through agriculture and industrialization. The black bear (Ursusa americanus) is a severely impacted species which has experienced nearly a 50% range loss and has been extirpated from parts of North America. Much public controversy surrounds this species, due to issues associated with hunting, human-bear interactions, and species management strategies. Increasing public awareness of black bear ecology, a charismatic megafauna, will spark positive and pro-conservation efforts across North America. Therefore, area and knowledge-specific education is important in engaging affected communities in black bear ecology and the importance of conservation. The Ministry of Natural Resources has made an effort in these areas by establishing the Bear Wise Program, however there is room within this program for expansion and development of their initiatives. Such expansions include improvement of public education, community outreach and youth awareness of black bears in a holistic ecosystem, versus case specific, approach. Identification of high-impact areas in Ontario would support the allocation of provincial resources to address these issues by implementing changes in local infrastructure, educational development and region-specific management. This project builds off of already established black bear management to increase region and province-wide conservation. The knowledge provided through this project will allow for future efforts across a wide geographical area.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".