Exploring appropriate business models for establishment of water quality monitoring service in Newfoundland and Labrador
Bibliographic record
Abstract
The Government of Newfoundland and Labrador regularly tests public drinking \nwater supplies to ensure the absence of contaminants. Private water supplies, including \nwells, fall outside the mandate of this testing regime. Over 50,000 wells are estimated to \nbe in Newfoundland and Labrador servicing approximately one-fifth of the population. \nHaving identified this service gap, the following thesis seeks to explore two main \nobjectives: to assess and articulate the potential public health risk, and to explore business \nmodels for the establishment of a water quality monitoring service. A mixed-methods \napproach is taken, employing both qualitative data from interviews with health experts, \nmunicipal representatives, laboratory professionals and private well owners, and \nquantitative data in the form of a model for the potential exposure risk, and financial \nmodels exploring solutions. The result is a high-level business approach exploring the \nservice gap in water quality monitoring for private well owners in Newfoundland and \nLabrador.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".