Modelling The Transport and Return of Chloride Using INCA-Cl in an Urbanizing Watershed
Bibliographic record
Abstract
1.0 Introduction In northern environments such as Canada, road salt (e.g. sodium chloride, NaCl) has been used as a de-icing agent to improve winter driving conditions since the 1950’s (Godwin et al., 2003). While research has shown that the application of salt to roadways can reduce accident rates by up to 88%, the use of road salt has been linked to increasing concentrations of chloride (Cl) in ground and surface waters in urbanized watersheds (Godwin et al., 2003). A recent study (Dugan et al., 2017) which tested 371 lakes in north eastern North America found that 44% trended towards long term salinization – levels at which Cl concentrations may begin to impact freshwater ecosystems. High Cl concentrations have been found to be potentially lethal to aquatic organisms, and long-term exposure can have detrimental effects on human health (Howard and Beck, 1993; Kelly et al., 2008). Keeping lakes and rivers “fresh” is important for the maintenance of ecosystem services associated with freshwater resources such as drinking water, fisheries and aquatic habitat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".