Informing Resource Managers - Using Local Ecological Knowledge of Murre Hunters, A Case Study
Bibliographic record
Abstract
Thirty murre hunters in Bonavista Bay, Newfoundland and Labrador, Canada were interviewed during the 2021-22 hunting season to solicit their views respecting the federal regulatory framework in place to manage the hunt and to use hunter local ecological knowledge (LEK) to identify abundance and distribution changes in the study area. \nThe results suggest that while murre hunters believe they have historic rights to hunt murres, they generally support the regulations in place. Furthermore, hunters would be prepared to accept amendments to the existing regulations to support conservation efforts if they were provided with evidence that was consistent with their own observations. Murre hunter interviews suggest that hunter knowledge of murre biology is weak despite their extensive hunting knowledge and experience. \nLEK was used to ascertain hunter temporal and spatial knowledge of murre abundance and distribution in the study area. LEK was useful to identify temporal and spatial distribution and abundance within each murre hunter’s hunting territory but was limited as a methodology to quantify abundance and distribution changes within the broader context. Despite this, hunters observed in murre distribution and abundance anomalies within Bonavista Bay due to changes in bait distribution, increasing ocean temperatures, lack of ice over the past decade, hunter pressure, fishing impacts, and wind conditions that impacted their hunting success.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".