Movement and Aggradation of Eastern Hudson Bay Beluga Whales (Delphinapterus Leucas): A Comparison of Patterns Found Through Satellite Telemetry and Nunavik Traditional Ecological Knowledge
Bibliographic record
Abstract
"Traditional Ecological Knowledge (TEK) consists of the collective knowledge, experience, and values of subsistence communities, while Western science relies on hypothesis testing to obtain information on natural processes. Both approaches provide important ecological information, but few studies have directly compared the two. We compared information on movements and aggregation of beluga whales obtained from TEK interview records (n=3253) and satellite telemetry records of 30 whales tagged in eastern Hudson Bay, Canada, using geographic information system (GIS) approaches that allowed common formatting of the data sets. Estuarine centres of aggregation in the summer were evident in both data sets. The intensive use of offshore areas seen in the telemetry data, where 76% of the locations were more than 15 km from mainland Quebec, was not evident in the TEK data, where only 17% of the records indicated offshore locations. Morisita's index of similarity indicated that TEK and telemetry data distributions varied with season, with the highest similarity in winter (0.74). Location and movement data from the telemetry study were limited by small sample size and short tag deployment times, while TEK data were biased by spatial coverage and coastal travel habits. Although the two data sets can provide complementary information, both suffer from weaknesses that need to be acknowledged when these data are adapted for use in resource management."
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".