Seasonal interplay of discharge and phosphorus concentration: Year-round areal loads to western basin Lake Ontario from urban and urbanizing tributaries
Bibliographic record
Abstract
The export of phosphorus from watersheds to large temperate lakes is strongly linked to the seasonality of discharge from landscapes. Urbanization in watersheds alters flow dynamics and provides novel point and diffuse sources of nutrients, creating “fast landscapes” that augment nutrient export to receiving waters. Here, a combination of hydrologic event-based composite and baseflow grab sampling regimes were used at 13 Lake Ontario tributaries along a ∼ 140-km stretch of the Canadian shoreline from 2018 to 2023 to investigate seasonality in discharge, phosphorus, and suspended solid concentrations, and areal loads across watersheds that span a gradient of urbanization for southern Ontario. Rivers within watersheds with a higher degree of urbanization had elevated dissolved phosphorus (DP) concentrations (highest in warm seasons), areal discharge, and areal loads of DP, particulate phosphorus (PP), and suspended solids (SS) across all seasons. At the mixed-use and rural watershed rivers, export of DP, PP, and SS were chemodynamic with discharge, highest in spring and winter and relatively lower during summer and autumn months. The highly urbanized rivers likely have the greatest impact on Lake Ontario’s nearshore water quality as they provide a relatively consistent supply of phosphorus to the nearshore zone.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".