Community-led coastal ecosystem assessments in the Hudson Bay Complex (Igloolik, Kinngait, and Naujaat, NU and Whapmagoostui, QC): Synthesis of 2020-2021 field programs
Bibliographic record
Abstract
The Hudson Bay Complex (HBC) is rapidly changing, which is impacting ecosystems and Northern Indigenous communities. To address a knowledge gap in understanding coastal ecosystem, a community-led coastal assessment was completed in four HBC communities to assess the biodiversity of fishes, invertebrates, and their habitats in a program called “Arctic Coast”. Communities that participated in the Arctic Coast program from the HBC included: Kinngait, Naujaat, and Igloolik, Nunavut, as well as Whapmagoostui, Quebec. This coastal program captured seasonal and inter-annual differences within and among regions between 2020 and 2021. It also assists in enhancing community research capacity through training and leadership opportunities. This report summarizes species occurrences, biological information on collected species, and describes the habitat of each coastal ecosystem. Data collected across different communities indicates spatial and temporal variation in fishes, invertebrates, and environmental parameters. Overall, the greatest fishing effort took place in Kinngait, which is also where the highest number of fish were captured. Notably, Grubby Sculpin (Myoxocephalus aenaeus) was documented in Kinngait, and is the most northern location recorded for this species. During the open water period, the warmest daily average water temperature occurred in Whapmagoostui. Overall, the information documented in this report will provide a baseline in order to assess future change and may aid in the identification and assessment of culturally and ecologically important marine areas.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".