Yukon First Nation wildlife harvest data \ncollection and management : lessons learned and future steps
Bibliographic record
Abstract
The Yukon Umbrella Final Agreement was signed in 1993 and Chapter 16 allows Yukon First Nations to govern wildlife harvest on traditional territories. First Nation governments manage wildlife using traditional ecological knowledge and have started to collect harvest data to inventory wildlife use and incorporate in management. A workshop, hosted near Lake Laberge by Ta'an Kwäch'än, facilitated discussion amongst First Nation delegates regarding wildlife harvest data collection was conducted November 5 and 6, 2009. A questionnaire was conducted prior to the workshop to provide guidance for discussion topics. The workshop had four objectives: 1) understand the importance of First Nation harvest data and how the data will be used during management decisions, 2) discuss methods used to collect harvest data and potential for a unified approach, 3) discuss potential methods for storing data, protecting confidentiality while allowing effective management, and 4) produce a document that can be used to implement or improve harvest data collection. This project will fulfill the fourth objective by summarizing the workshop content, explore the factors that promote and hinder data collection, and the intermediate and long-term objectives that will allow First Nation governments to become effective co-management partners while ensuring their traditional lifestyle and connection to the land is not lost.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".