Prediction of arsenic removal in aqueous solutions with non‐neural network algorithms
Bibliographic record
Abstract
Abstract Despite some well‐known limitations of artificial neural network (ANN), the basic ANN and its derivatives are frequently used to predict the removal efficiency of different heavy metals like arsenic (As) using various adsorbents, including bio‐adsorbents. Notable examples of applying non‐neural network (NN) approaches, such as support vector regression (SVR) and random forest (RF), are almost non‐existent in theliterature. In the current study, the suitability of these modules to predict As removal efficiency was investigated based on seven independent experimental data sets. The SVR and RF experiments with the merged datasets of experimental and interpolated data points demonstrated their effectivity for the prediction. Specifically, the RF method achieved 97.3% Spearman's rank correlation coefficient (SRCC) and 95.9% R 2 on average, whereas its SVR counterpart exhibited average performances of 96.3% SRCC and 93.9% R 2 on a hold‐out test set. A general method based on the natural cubic spline technique was introduced to interpolate the experimental data points. Compared to the ANN methodology, the non‐NN approaches involved tuning fewer hyperparameters and easier training processes in the R open‐source framework. Furthermore, this kind of economical application of the machine learning algorithms allowed ranking the experimental parameters based on their relative contribution in the As removal efficiency. The study showed that the following parameters are the most influential ones, in decreasing order: pH, adsorbent dose, temperature, agitation time, and initial As concentration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".