Decomposition of canola stubble by solid-state fermentation with Cyathus olla
Bibliographic record
Abstract
Cyathus olla is being studied for its potential as an inoculant to accelerate decomposition of canola stubble, and hence to reduce the incidence of stubble-borne diseases of this crop. Stubble infested by C. olla appears soft and macerated, but the extent of decomposition incited by this fungus is not known. Composition of stubble fiber of five canola cultivars was determined with the Goering Van Soest method of fiber determination. Sterile canola (cv. Cyclone) substrate was inoculated with C. olla and incubated at 25 C for 45 days, followed by fiber analyses to detect changes in the fiber content. All cultivars were variable with respect to cell wall composition. Canola substrate inoculated with C. olla f. brodiensis had 60.6% of the original lignin remaining after incubation, compared to 65.9% for the substrate incubated with C. olla f. olla and 71.8% with C. olla f. anglicus. Hemicellulose content was reduced as only 75.3, 78.6, and 81.6% of the original hemicellulose content remained after incubation with C. olla f. brodiensis, C. olla f. olla, and C. olla f. anglicus, respectively. Cellulose was also degraded, and the neutral detergent soluble fraction increased. Cyathus olla was capable of degrading lignin in vitro, but field testing must follow to assess its decomposing activity under natural conditions.
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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 | 0.000 | 0.000 |
| 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".