Optimization of the parameters impacting the Direct Red 89 degradation with thermally activated persulfate using a full factorial design
Bibliographic record
Abstract
A two-level full factorial design with interactions was effectively used to screen for parameters impacting the degradation of Direct Red 89 (DR89) by thermally activated persulfate. Four variables were identified as critical: reaction temperature, persulfate concentration, initial pH of medium, and initial DR89 concentration. The goodness of fit of the reduced model was tested by generating plots of descriptive statistic, residuals versus predicted responses, normal probability versus residuals, and observed versus predicted values, as well as examining the analysis of variance table. The observed and the predicted response values of the reduced model exhibited a good correlation, with R2, [Formula: see text] , Q2, and p of 0.990, 0.983, 0.968, and 0.000, respectively. To determine optimal operating parameters, the desirability function was utilized, and it was determined to be 0.988 with a predicted response of 99.89% for an initial DR89 concentration of 51.96 mg/L, a persulfate concentration of 12 mmol/L, a reaction temperature of 60 °C, and a pH of 3.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".