Recent Advances in the Analysis of Real-time Water Quality Data Collected in Newfoundland and Labrador
Bibliographic record
Abstract
A real-time water quality monitoring (RTWQM) network was established in the province of Newfoundland and Labrador in late 2001. The network has changed the way river health is assessed in the province and a great deal has been learned in recent years about using this innovation in resource management. This paper summarizes three new developments carried out in recent years using RTWQM data. First, regression models are developed using real-time data as a surrogate for the concentration of important indicators of water quality that have traditionally been determined through manual grab sample collection. Second, regression models are developed for the prediction of water temperature and dissolved oxygen at the real-time water quality stations. A graphical approach is presented that links air temperature to these two important indicators of water quality. Third, control charts are investigated as a means of analyzing the data collected by the network. These charts have traditionally been used in the manufacturing and processing industries, where their usefulness as a quality control tool hinges upon the assumption that observations from the process being monitored are independent random variables. RTWQM measurements are autocorrelated over time and this lack of independence poses a challenge for control chart design. While a time-series approach is suitable for studying short subsets of the data (e.g. hourly measurements collected over the course of 3 to 5 days), the resulting chart does not clearly show when the health of an aquatic ecosystem is being threatened. Replacing the traditional control chart limit lines in favor of water quality criterion limits that better represent the concerns of resource managers is a much more suitable approach to analyzing real-time data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".