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Record W7115692372 · doi:10.25976/v9vy-0e05

Kivalliq Inuit Association and the Department of Crown-Indigenous Relations and Northern Affairs Canada Joint Water Sampling Program

2025· dataset· en· W7115692372 on OpenAlexaboutno aff

Bibliographic record

VenueDataStream · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsJoint (building)MemorandumBaseline (sea)Work (physics)Sampling (signal processing)Water qualityCommission

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.094
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0370.016

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.

Opus teacher head0.010
GPT teacher head0.240
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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