Data Quality from a Community-Based, Water-Quality Monitoring Project in the Yukon River Basin
Bibliographic record
Abstract
This paper examines the quality of data collected by the Indigenous Observation Network, a community-based water-quality project in the Yukon River Basin of Alaska and Canada. The Indigenous Observation Network relies on community technicians to collect surface-water samples from as many as fifty locations to achieve their goals of monitoring the quality of the Yukon River and major tributaries in the basin and maintaining a long-term record of baseline data against which future changes can be measured. This paper addresses concerns about the accuracy, precision, and reliability of data collected by non-professionals. The Indigenous Observation Network data are examined in the context of a standard data life cycle: plan, collect, assure, and describe; as compared to professional scientific activities. Field and laboratory protocols and procedures of the Indigenous Observation Network are compared to those utilized by professional scientists. The data of the Indigenous Observation Network are statistically compared to those collected by professional scientists through a retrospective analysis of a set of water-quality parameters reported by all three projects over a number of years. No statistical differences were found among the three projects for pH, Calcium, Magnesium, or Alkalinity, although statistically significant differences were found for Sodium, Chloride, Sulfate, and Potassium concentrations. The statistical differences found were small and likely not significant in terms of interpreting the data for a variety of uses. Our results suggest that Indigenous Observation Network data are of high quality, and with consistent protocols and participant training, community based monitoring projects can collect data that are accurate, precise, and reliable.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".