GREEN STRATEGY FOR RECLAIMING ECOSYSTEM: MICROBIAL REMEDIATION OF LEATHER INDUSTRY WASTES
Bibliographic record
Abstract
Meeting environmental regulations for both liquid and solid wastes are produced during the manufacture of leather items is one of the long-term issues facing the leather industry. Insufficient treatment of these wastes will cause environmental pollution and endanger human health. Trimmings have generally been underutilized among other trash that are produced. Hair is not utilized, however collagen found in trims and garbage. Many organic and inorganic particles together with the discharge of suspended or gas-solid oil and grease, nitrogen-containing compounds, and heavy metals either by themselves or in their reduced salt form, chlorides, sulphates, chemical oxygen demand (COD) and total dissolved solids (TDS) are all considerably generated and influenced by tanning operations. Formaldehyde used in the production of finished leather that are difficult to biodegrade and can cause the production of free formaldehyde, a recognized carcinogen. Microbial bioremediation is a novel technique that may be used in a variety of soil and water environments due to microorganisms' adaptability to remove hazardous pollutants that could offer a safer and affordable strategy. The pollution profile of leather industries, microbial bioremediation for pollution reduction from diverse ecological lattices and interactions between the microbes and contaminants has received substantial attention in this review.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".