IMALIRIJIIT: a community-based environmental monitoring program in the George River watershed, Nunavik, Canada
Bibliographic record
Abstract
There is increasing interest in community-based environmental monitoring (CBEM) in Canada’s North in response to the rising impacts of resource exploitation and climate change, and with increased recognition of indigenous knowledge. IMALIRIJIIT, meaning those who study water in Inuktitut, is a CBEM program involving science land camps, capacity-building workshops, and scientific data collection with the participation of youth, elders, local experts, and researchers. It was coinitiated by the Inuit community of Kangiqsualujjuaq (Nunavik, Quebec) and university researchers. This hands-on and land-based program aims to establish a sustainable environmental monitoring program of the George River, before the start of a rare earth elements (REEs) mining project in its upper watershed. The community was concerned about potential impacts on the river, as it is crucial to fishing, hunting, and gathering. The community therefore wanted its own independent and long-term environmental monitoring program to collect baseline data and promote local capacity-building. IMALIRIJIIT includes water-quality measurements, bio-indicators, contaminant and REE biomonitoring in traditional food, remote-sensing analysis of water-quality parameters and vegetation change at the watershed scale, as well as interactive mapping of traditional ecological. IMALIRIJIIT outcomes and challenges are discussed to identify conditions for successful implementation of CBEM and environmental stewardship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".