Secretion of keratinolytic enzymes and keratinolysis by<i>Scopulariopsis brevicaulis</i>and<i>Trichophyton mentagrophytes</i>: regression analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it