What we heard: summary of community meetings to propose a new beluga tagging program in the Inuvialuit Settlement Region
Bibliographic record
Abstract
Community meetings were held across the Inuvialuit Settlement Region in November 2017 to seek input and support toward a proposed new beluga satellite telemetry (‘tagging’) program. Rationale for the program included the need for new movement and dive data to support an updated population abundance estimate for the Eastern Beaufort Sea beluga, and to gain a better understanding of patterns and drivers of beluga movement and habitat use in light of climate change and increased vessel traffic in the region. Capture and tagging of belugas contravenes Inuvialuit cultural norms to respect wildlife and many concerns were raised about the tagging methods. The current report summarizes questions, concerns, knowledge and suggestions shared by Inuvialuit during community engagement meetings. Key recommendations to address these concerns were to include Inuvialuit in the Animal Care review process, to invest in the development of a less invasive tagging method, and to learn more about the impacts of tagging on belugas. Meeting participants also identified research questions and provided input toward study design, field logistics, and plans for communication and results dissemination. An additional outcome of the community meetings was the establishment of the Tagging Advisory Group, a project-level steering committee comprised of scientists and beluga harvesters. The Tagging Advisory Group was formed to engage Inuvialuit Knowledge holders throughout the design and delivery phases of the program, and to move from a consultative relationship toward a more collaborative science partnership.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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".