An ensemble modeling framework to elucidate the regulatory factors of chlorophyll-a concentrations in the Nanji wetland waters of Poyang Lake
Bibliographic record
Abstract
Chlorophyll-a (Chl a) is an important indicator of algal biomass frequently used to evaluate the severity of cultural eutrophication. Identifying the key covariates of Chl a concentrations is essential to understand the mechanisms that drive eutrophication and to develop forecasting tools that guide the restoration process. In this study, we present a novel ensemble modeling framework founded upon the complementary features of Random Forest (RF) and Generalized Additive modeling (GAMs). A series of RF models are first developed to forecast Chl a concentrations based on the antecedent values of a multitude of environmental predictors. GAMs are then used to explore the presence of non-linearities in the seasonal relationships between Chl a and the identified predictors. The optimal RF models using a 0–8 day time lag displayed high predictive skills with adjusted R2 values consistently above 0.80. Analyses of the RF models revealed that the modulating factors of Chl a display significant seasonality. Dissolved oxygen (DO) and turbidity were the key covariates of Chl a in the spring, while the water level fluctuations predominantly regulated phytoplankton biomass in the summer and winter. The occurrence and severity of algal blooms in the summer and autumn were associated with threshold levels of 0.06 and 1.50 mg/L for total phosphorus (TP) and total nitrogen (TN) concentrations, respectively. These results reveal the potential of the introduced modeling framework to shed light on the regulatory factors of algal biomass as well as to establish real-time predictions in the Nanji wetland waters of Poyang 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".