Geostatistical modelling of in-stream chloride concentrations across seasonal flow states in three urbanizing watersheds
Bibliographic record
Abstract
Road salt causes increasing environmental chloride (Cl-) concentrations which threaten aquatic ecosystems. Environment Canada recommends that road authorities should develop salt management plans including identification of salt vulnerable areas. In this thesis, a Spatial Stream Network (SSN) geostatistical modelling approach was used with seasonal longitudinal field data to develop reach scale models of in-stream Cl- concentrations in three Southern Ontario watersheds. Significance of potential drivers (lane length density (LLD), agricultural & undifferentiated rural land (AURL), and permeability of surficial geology) of stream Cl- were assessed. Results suggest that SSN models are not consistently better than Euclidean models across watersheds. Unexpectedly, LLD was the most important predictor in the rural watershed, and AURL was most important in the urban watershed. Results also show that spatial structure in stream Cl- concentrations was lost under higher flow conditions, which has important implications for when data should be collected to map salt vulnerable areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".