Modeling of Water Quality Dynamics Using Indigenous Knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unavailability of western-laboratory-type data on water quality for the areas where the aboriginal people live requires developing special evaluation and prognosismaking methodologies. To determine the key parameters of the water quality we interviewed the experts (aboriginal elders). Basing on the determined key parameters we formed the key questions and developed the questionnaires. The questionnaires were distributed among the households of the Peepeekisis and Kahkewistahaw aboriginal communities (Saskatchewan, Canada). According the developed model we can conclude that within next 15-25 years the negative dynamics of the water quality in both of the communities will be held. However if the positive factors (construction of water treatment facilities, restriction of chemical use, etc) regarding the water quality are maintained at least at the current level, in 15-25 years we may observe dominating of positive trends in water quality in both of communities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it