Application of mixture design for compound microbial inoculants in straw degradation.
Bibliographic record
Abstract
Crops straw contains large quantities of lignocelluloses that have stable structure and hardly degraded.Apply mixture design to study different mixtures of five strains that can degrade lignocelluloses.Regression model on microorganism composition and degrading rate of lignocelluloses and content of reducing sugar was established.The predictable degrading rate of cellulose reached 35.75%.The maxim predictable degrading rate of lignin was 27% and the most predictable production of reducing sugar was 3.39mg/g.Based on these response values satisfied all expectation were optimized,and the most excellent combinations of Bacillus Subtilis,Bacillus Licheniformis,Canadian krusei,Trametes versicolor,Phaerochaete chrysosporium were 12.1%,10%,27.2%,10.6%,and 40%,respectively.Three indicators of the measured values were 35.47%,26.41% and 2.37mg/g.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".