Remedial strategy of algal proliferation in a regulated river system by integrated hydrological control: an evolutionary modelling framework
Bibliographic record
Abstract
We simulated water-quality measures in a regulated river system (the lower Nakdong River) under simultaneous discharge control at upriver dams and an estuarine barrage with the goal of reducing phytoplankton biomass (chlorophyll a concentration). We used genetic programming (GP) to create a rule-set-based predictive model for the chlorophyll a concentration based on 16 years (1994–2009) of meteorological, hydrological, and limnological data. The rule-set model used eight variables, including water temperature, dam and estuarine barrage discharge, phosphate and silica concentrations, and accurately predicted the phytoplankton biomass (determination coefficients, r2, for training and test data were 0.52 and 0.45, respectively). According to sensitivity and scenario analyses, a larger water volume resulting from increased discharge from upriver dams and decreased discharge from an estuarine barrage would reduce chlorophyll a concentrations at the study site. This result provided ample evidence that simultaneous manipulation of dam and estuarine discharge rates could effectively increase river flow and flush aggregated algal populations downstream. Additionally, we considered that even small increases in river flow could play a role in diluting phytoplankton biomass during the dry winter season when estuarine discharge remains low. These two hydrological mechanisms could be used as selective strategies for water-resource management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".