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Record W2165890965 · doi:10.7202/706202ar

Decomposition of canola stubble by solid-state fermentation with Cyathus olla

2005· article· en· W2165890965 on OpenAlexfundvenueno aff
T.C. Shinners-Carnelley, J. P. Tewari

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHemicelluloseCanolaBiologyLigninBotanyAgronomyHorticulture

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.230
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2005
Admission routes2
Has abstractyes

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