Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake
Bibliographic record
Abstract
Rapid global urbanization has led to water eutrophication, threatening the stability of aquatic ecosystems stability. Chlorophyll-a (Chla), a key indicator of algal biomass, is a widely recognized as a metric for eutrophication. However, existing remote sensing retrieval methods face limitations in addressing complex environmental variations. This study developed an innovative Dynamic Model Pool (DMP) framework to optimize water quality prediction performance dynamically. Using Sentinel-2 satellite imagery and monthly in-situ Chla measurement data from Erhai located in Southwest China spanning 2018 to 2020, this study tested the effectiveness of the DMP framework. The results demonstrated that: (1) The DMP framework dynamically selected the optimal model based on data-specific characteristics. In 2018, the CBR model achieved the highest accuracy, while in 2019, GBR and XGBR were the most accurate. In 2020, GBR outperformed other models. (2) Spatiotemporal Chla distribution maps recorded consistently higher concentrations in the south part of lake, while the central part showed minimal level and variation. (3) Seasonal precipitation and temperature variations and policy implementation were key drivers of Chla concentration changes. Seasonal variations in precipitation and temperature collectively influenced the nutrient input and dilution dynamics in Erhai. Meanwhile, policy interventions implemented between 2018 and 2022, such as pollution interception and wastewater treatment, substantially decreased nutrient inflows during flood seasons and effectively limited nutrient accumulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".