Assessing the Impact of Nutrient Levels (N:P Ratios) and Temperature, on Algal Growth in an Urban Lake through Monitoring
Bibliographic record
Abstract
Large numbers of urban lakes are facing issues related to cyanobacteria, commonly known as bluegreen algae, resulting in significant threats to humans and animal health due to their production of microtoxins and Anatoxin-A.These toxins induce acute effects including gastrointestinal distress, and respiratory problems, underscoring the need to protect urban lakes to safeguard community health and ecosystem well-being.A comprehensive monitoring program is a critical step to address cyanobacteriarelated issues.Monitoring results of dissolved phosphorous, nitrates(N), Total Kjeldahl Nitrogen (TKN), total phosphorous (TP), TSS, and Escherichia coli (E.coli) concentrations, are used to understand the importance of different pathways events during dry conditions (no prior rainfall) and wet events (rainfall during or proceeding monitoring events) plus a microbial source tracking procedure, to understand impacts on growth characteristics of cyanobacteria and microtoxins.The Mann-Kendall Trend Test is used to characterize trends of various parameters (using transitions of 'wet' and 'dry' to the Lake and within the Lake), to identify the impact of various management strategies and/or the impact of various sources from overland land uses for a) wet events to the Lake, b) dry events to the Lake, c) wet events in the Lake, and d) dry events within the Lake.Microbial source tracking methods are used along the shoreline of Fairy Lake to interpret elevated levels of fecal pollution and E. coli bacteria in water samples, aiding in the identification of pollution sources.Blue-green algae have been observed at least once each year from 2018 to 2023.Microtoxins and Anatoxin-A were present in nine samples with a maximum of 1.72mg/L and 0.02mg/L, respectively.Frequent beach closures have occurred every year, raising public concerns regarding both ecological and recreational aspects for Fairy Lake.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".