Recent Advances in Enzymes for the Bioremediation of Pollutants
Bibliographic record
Abstract
Nowadays, pollution of the environment is a huge problem for humans and other organisms' health. Conventional methods of pollutant removal like membrane filtration or ion exchange are not efficient enough to lower the number of pollutants to standard levels. Biological methods, because of their higher efficiency and biocompatibility, are preferred for the remediation of pollutants. These cost-effective and environment-friendly methods of reducing pollutants are called bioremediation. In bioremediation methods, enzymes play the most crucial role. Enzymes can remedy different types of organic and inorganic pollutants, including PAHs, azo dyes, polymers, organocyanides, lead, chromium, and mercury. Different enzymes isolated from various species have been used for the bioremediation of pollutants. Discovering new enzymes and new subtypes with specific physicochemical characteristics would be a promising way to find more efficient and cost-effective tools for the remediation of pollutants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".