What we heard: community meetings for Beluga and Bowhead tagging and aerial survey programs planned for 2019 in the Inuvialuit Settlement Region
Bibliographic record
Abstract
Three large-scale marine mammal science programs were proposed to take place in the Inuvialuit Settlement Region in July and August 2019. These included Canadian-led beluga tagging and aerial survey programs to update the abundance estimate for the Eastern Beaufort Sea beluga population, and a US-led aerial survey to estimate the abundance of the Bering-Chukchi-Beaufort Seas bowhead population. In November 2018, Fisheries and Oceans Canada organized public meetings in all six communities in the Inuvialuit Settlement Region to (1) share preliminary results from the 2018 beluga tagging program and discuss plans for beluga tagging in 2019; (2) share proposed plans and survey designs for beluga and bowhead aerial surveys in July and August 2019, respectively; and (3) seek community input into the design of all three science programs. This report describes meeting planning and logistics, highlights key information shared during the science presentations, and summarizes the feedback received by project proponents from Inuvialuit participants who attended each community meeting. Information shared by meeting participants was used to refine study designs and was one way that Inuvialuit Knowledge and perspectives guided the research programs.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".