Secretion of keratinolytic enzymes and keratinolysis by<i>Scopulariopsis brevicaulis</i>and<i>Trichophyton mentagrophytes</i>: regression analysis
Bibliographic record
Abstract
A survey on keratinophilic fungi from poultry-farm soils at Namakkal and from feather dumping soils at Chennai, India, revealed the existence of 34 species of fungi. Most of the fungi exhibited variable efficiency in producing extracellular keratinase when grown in plates with chicken feathers as the sole carbon and nitrogen source. The fungi Aspergillus flavus, Aspergillus niger, Aspergillus versicolor, Chrysosporium state of Arthroderma tuberculatum, Paecilomyces carneus, Scopulariopsis brevicaulis, Trichoderma viride, and Trichophyton mentagrophytes were efficient candidates to degrade the feathers. However, when cultivating the strains in submerged conditions in a medium containing chicken feathers as the sole nutrients source, Aspergillus glaucus, Chrysosporium keratinophilum, Curvularia lunata, Fusarium solani, and Penicillium citrinum also proved to be potent. Among all species, S. brevicaulis and Trichophyton mentagrophytes produced higher amounts of keratinase in both methods. Conditions for keratinase production were optimized by statistical design and surface plots. The highest keratinase activity was estimated by S. brevicaulis (3.2 KU/mL) and Trichophyton mentagrophytes (2.7 KU/mL) in the culture medium with chicken feathers and shows (79% and 72.2% of degrading ability, respectively).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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, 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".