Trophic state decrease after lanthanum-modified bentonite (Phoslock) application to a hyper-eutrophic polymictic urban lake frequented by Canada geese (<i>Branta canadensis</i>)
Bibliographic record
Abstract
Urban lakes are important assets to highly populated regions; however, extensive usage and other influences degrade their water quality, which then requires rehabilitation and maintenance. Hyper-eutrophic Swan Lake, Greater Toronto, Canada (5.5 ha, 4.4 m maximum depth) was a gravel pit that became degraded by elevated total phosphorus (TP) concentrations, mostly from internal P sources. Because Swan Lake is a terminal lake with limited flushing and small external load, a phosphate adsorbing and sediment capping agent, lanthanum-modified bentonite (Phoslock), was applied in spring 2013 to intercept the internal load. Average TP concentration decreased from 0.247 to 0.099 mg/L in the first and 0.060 mg/L in the second post-treatment year. A TP mass balance model adequately predicted post-treatment annual average TP concentration by not including the pre-treatment internal load estimate of 650 to 1100 mg/m2/yr. Phytoplankton biomass decreased only in the second post-treatment year, when Secchi transparency (highly correlated with chlorophyll concentration) increased to a growing season average of 1.4 m (range 0.7–2.7) compared to 0.5 m (0.37–0.63) before treatment. We explain the lack of response in the first treatment year with a relatively late application (29 Apr–1 May 2013), when P released from the winter bottom sediments had already been taken up by phytoplankton. Recently, a growing population of waterfowl (mostly Canada goose, Branta canadensis) were the highest contributors of nutrients (75%), as indicated by a mass balance based on literature-derived goose P export and biweekly bird census. We recommend waterfowl management or repeated treatment to further improve water quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".