Limnological assessment of the meteo-hydrological and physicochemical factors for summer cyanobacterial blooms in a regulated river system
Bibliographic record
Abstract
This study aimed to explain how the changes in certain hydrological, meteorological and physicochemical factors influence the cell density of the cyanobacteria Microcystis aeruginosa in the Nakdong River. Occurrence patterns of M. aeruginosa were analyzed between 1993 and 2010 (N=96) using a self-organizing map. The cell density of M. aeruginosa was sensitive to certain meteorological, hydrological and physicochemical factors. In addition, our clustering analysis results identified specific limnological features under different environmental conditions. Cluster 1 suggested that high rainfall and increased river flow, dam discharge, total phosphorous and phosphate concentrations were associated with low M. aeruginosa cell density (June–July; monsoon season). However, cluster 2 suggested low irradiance since water temperature decreases with irradiation time, and thus low M. aeruginosa cell density (April–June and after November). Finally, cluster 3 was indicative of high water temperature and irradiance, increased irradiation time, low phosphate and nitrate concentrations, and high M. aeruginosa cell density (August, after the monsoon season). Taken together, these results suggest that rainfall, river flow, water temperature and nutrient concentration (i.e., phosphates and nitrates) were the primary factors that affected cyanobacterial bloom occurrence in the Nakdong River. M. aeruginosa blooms can be suppressed by employing an integrated water resource management program that accommodates meteo-hydrological factors along with the effective control of exogenous nutrient sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".