Phosphorous runoff risk assessment and its potential management using wollastonite according to geochemical modeling
Bibliographic record
Abstract
Abstract Loss of phosphorus from agricultural land through water runoff causes serious detrimental effects on the environment and on water quality. Phosphorous runoff from excessive use of fertilizers can cause algal blooms to grow in nearby water systems, producing toxins that contaminate drinking water sources and recreational water. In this study, a risk analysis of the algal toxin micro-cystin-LR and the mitigation of phosphorus from agriculture runoff is discussed. A risk analysis was performed on the algal bloom toxin microcystin-LR considering the Lake Erie algal bloom event of 2011 as a case study. Toxicity risk analysis results show that relatively low concentrations of microcystin-LR compared to recent case studies pose an acute health risk to both children and adults, and a significant increase in the risk of developing cancer is suggested but subject to further study given the assumptions made. This study investigated the potential of using wollastonite to mitigate phosphorus pollution, considering thermodynamic conditions of a constructed wetland receiving influent water from agriculture runoff, by using geochemical modelling. Geochemical modelling results show that wollastonite can react with phosphorus and capture it in the stable mineral form of hydroxyapatite, offering a possible strategy for risk mitigation of phosphorous runoff. A removal efficiency of 77% of phosphorus using wollastonite is calculated with the help of geochemical modelling.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".