Color and Toxicity Removal following Tyrosinase‐Catalyzed Oxidation of Phenols
Bibliographic record
Abstract
The products of phenol oxidation catalyzed by mushroom tyrosinase (polyphenol oxidase, EC 1.14.18.1) were assessed in terms of their residual color and toxicity. The addition of aluminum sulfate had little effect on the removal of colored products from phenol solutions treated with tyrosinase. Although chitosan was used successfully to remove the color when added before the reaction initiation or after the reaction completion, the required dose of chitosan was lower when it was added after the reaction. In this case, the minimum doses of chitosan required to achieve 90% color removal were proportional to the logarithm of the initial concentration of phenol. The color removal induced by chitosan addition appeared to be the result of chemical interaction followed by a coagulation mechanism. All treated solutions of phenol and chlorophenols, except 2,4-dichlorophenol, had substantially lower toxicities than their corresponding initial toxicities, as measured using the Microtox assay. Chitosan addition significantly enhanced the reduction in toxicity. The toxicities of the phenol solutions treated with tyrosinase were markedly lower than previously reported toxicities of solutions treated with peroxidase enzymes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".