Using a conductivity–alkalinity relationship as a tool to identify surface waters in reference condition across Canada
Bibliographic record
Abstract
Abstract The underlying natural relationship between conductivity and alkalinity was used to identify surface water quality monitoring sites that are in a ‘reference’ or minimally disturbed condition. Data from over 40,500 freshwater samples from 1,230 sites were combined for the time period of 2005–2015 from various federal, provincial, and joint federal–provincial/territorial freshwater monitoring programs (e.g., Freshwater Quality Monitoring and Surveillance Program, Ontario's Provincial Water Quality Monitoring Network). Of the samples, 30,347 provided conductivity and alkalinity data. Surface water samples with a measured conductivity that deviated (by more than 41 μS/cm) from the predicted conductivity calculated from the sample's alkalinity were deemed to be non-representative of a reference condition, while samples within 41 μS/cm of the predicted value were deemed representative of a reference condition. The 41 μS/cm cutoff value was determined using signal detection theory. The conductivity–alkalinity model was validated through a comparison with land cover data by demonstrating that samples identified as ‘reference’ were typically from catchments that had minimal anthropogenic disturbances. The proposed approach provides a rapid means of evaluating the reference condition of a watercourse, and of identifying data that provide an estimate of reference condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".