Kivalliq Inuit Association and the Department of Crown-Indigenous Relations and Northern Affairs Canada Joint Water Sampling Program
Bibliographic record
Abstract
The water quality data was collected by Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC) and the Kivalliq Inuit Association (KIA) through a Memorandum of Agreement (MoA) from 2012 to 2023 during their joint field program. The purpose of the MoA is to adapt and sustain the water quality monitoring program, with specific goals to document baseline conditions, determine natural variability, support the analysis of cumulative impacts, and identify changes in water flowing through Inuit-owned lands in areas of the Kivalliq Region where there is mineral development or potential for such development. The MoA sampling program allows CIRNAC to build upon its role as a federal manager of water resources and work with the KIA in their assessment of water resources, as per Article 20 of the Nunavut Agreement, while sharing the costs of this program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".