Optimizing water quality sampling through application of real time ionic concentration regression models
Bibliographic record
Abstract
The Water Resources Management Division of the Department of Environment and Conservation performs routine water sampling to measure the physical and chemical parameters of select water bodies in Newfoundland and Labrador. Ionic concentration parameter measurement is performed during routine water sampling to complement some of the key indicator parameters measured in real time at these select water bodies. The collection, laboratory analysis and measurement of water samples is a time consuming process. Some of the common conducting ions measured during routine sampling are sodium, calcium, chloride and sulphate. These conducting ions can be estimated using continuously measured specific conductance after observing the effect of flow. The estimated measurement will help identify whether any local stressors are affecting the quality of water at a given point in time and hence save time and resources in performing routine sampling. This can also be applied in remote locations where routine sampling is not feasible. This paper compares four water bodies on the island part of Newfoundland and Labrador and estimates the ionic concentration using continuously measured specific conductance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".