Modelling Metribuzin Removal Efficiency Through Adsorption Using Activated Carbon of Olive-waste Cake
Bibliographic record
Abstract
Abstract A simple mathematical model is developed for the prediction of Metribuzin removal efficiency through adsorption using activated carbon of olive-waste cake for any combination of input conditions. Based on earlier experimental results, factors from three independent variables (pH, initial Metribuzin concentration and adsorbent dose concentration) were incorporated in the model. All the factors are multiplied to derive a combined diminishing factor, which is multiplied with maximum achievable removal efficiency. It is found that although the model results are having good correlation (0.92) with the experimental results, those are slightly away from the ideal line. Through the introduction of an adjustment factor, model predictions are closely matching with the measured values having a correlation coefficient of 0.96. The primary model predicted results are having standard errors as RMSE = 6.34, MAE = 5.99 and RAE = 0.07, whereas the same error statistics of the adjusted model are 1.97, 1.71 and 0.01, respectively. Such modelling technique will predict removal efficiency for any combination of input parameters, which at times are required to be changed for other constraints.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".