Examination of Multiple Linear Regression (MLR) and Neural Network (NN) Models to Predict Eutrophication Levels in Lake Champlain
Bibliographic record
Abstract
Eutrophication is one of the main causes of the degradation of lake ecosystems. In this paper, multiple linear regression (MLR) and neural network (NN) methods were developed as empirical models to predict chlorophyll-a (Chl-a) concentrations in Lake Champlain. The models were developed using a large dataset collected from Lake Champlain over a 24-year period from 1992 to 2016. The dataset consisted of monitoring depth (Depth), total phosphorus (TP), total nitrogen (TN), alkalinity (RegAlk), temperature (TempC), chloride (Cl) and secchi depth (Secchi). Statistical analyses showed that TP, Secchi, TN and Depth demonstrated strong relationships with Chl-a concentrations. The simulation results revealed that both the MLR and NN models performed well in predicting Chl-a concentrations, especially for low to moderate concentrations of Chl-a ( 7.5 μg/L). These models can be useful for improving lake management and providing early warnings regarding the problem of eutrophication.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".