Assessing and predicting Lake Chloride Concentrations in the Lake-Rich Urbanizing Halifax Region, Canada
Bibliographic record
Abstract
Halifax, Nova Scotia, Canada. Many lakes in the Halifax region are approaching or exceeding the chronic freshwater aquatic life guideline for chloride (Cl-), presumably due to the application of deicing salts. These exceedances represent an ecological risk that requires mitigation for lakes currently experiencing high Cl-, and preventative steps should be taken to protect lakes that will be impacted by future development. In this study we paired geospatial analysis with linear regression methods to identify key factors contributing to elevated Cl- in Halifax lakes, and applied a mass balance modeling approach to estimate annual Cl- loading rates for dominant land uses within the region. The watershed variables found to be most predictive of mean lake [Cl-] were the % urban coverage, road density, and stormwater pipe density in a watershed. Annual Cl- loading rates for the four primary land use categories in the region were: rural 1–3 g/m2/yr, commercial 0–9 g/m2/yr, residential 97–162 g/m2/yr, and roads 804–964 g/m2/yr. The mass balance model developed in this study could be used to predict Cl- loading associated with planned developments, and subsequent impacts on receiving lakes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".