Indigenous owned aquatic monitoring programs in the Inuvialuit Settlement Region work: an adaptive framework with example traditional and local knowledge quotes and coastal marine water quality data collected from 2019 to 2022 near Sachs Harbour and Ulukhaktok
Bibliographic record
Abstract
Canadian Arctic residents are experiencing longer open water seasons and observing significant change in aquatic species and habitats. Rapid ecosystem change threatens Inuit peoples food security and adds to existing generational trauma caused by ongoing colonialism and industrialization. Indigenous owned environmental monitoring programs prioritize safety, support community empowerment, and self-determination to increase the understanding of and resilience to the impacts of this human caused global climate crisis. To this end, in 2019 local expert fishers and hunters from the Inuvialuit Settlement Region worked collaboratively with an independent research scientist to start up community owned aquatic monitoring and observation (AMO) programs in Sachs Harbour and Ulukhaktok in the Inuvialuit Settlement Region. The field teams received training to collect year-round data of lake and coastal water quality data using scientific instruments that measure water temperature, conductivity/salinity, turbidity, and dissolved oxygen with depth from the surface down to 150 m. ROV’s, drones, and hydrophones extend observations and traditional and local knowledge directs the programs. External clients can hire the teams to augment core funding. AMO teams are finalizing their program and data ownership protocols, which are informed by the Canadian UNDRIP Act and OCAP® principles. Indigenous owned, directed, and operated environmental monitoring programs justly deserve funding priority and ongoing public support to help conserve and protect critical areas for future generations.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".