Community experiences and perceptions of aquatic change in ᑭᙵᐃ <sup>ᑦ</sup> , ᓄᓇᕗ <sup>ᑦ</sup> (Kinngait, Nunavut)
Bibliographic record
Abstract
Climate change and development are shaping Arctic ecosystems in unprecedented ways that are intimately known to Inuit. To document changes in aquatic habitats and species near Kinngait, Nunavut, researchers co-created a questionnaire with the Aiviq Hunters and Trappers Association and community technicians. Inuit knowledge, centered on experiences and perceptions of marine, coastal, and lacustrine shifts, was gathered from 39 knowledge holders. Responses indicated that across ecosystems, turbidity and waves are not likely changing, wind and erosion may be changing, and water is warming. Ice is thinner, breaking up earlier, forming later, and diminishing in extent. These shifts are altering harvest timing in the spring and winter, and are rendering travel on the land increasingly difficult. While most knowledge holders reported no change in the diversity and abundance of marine mammals, fishes, and invertebrates, others expressed that ringed seal and beluga whale may be declining, salmon are appearing, and mussels are proliferating. Inuit insights and voices consolidated through this endeavour will serve the community and contribute to a baseline of knowledge to help understand ongoing change.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".