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Record W1979585039 · doi:10.1080/07060660509507223

Evaluating fungi from wood and canola for their ability to decompose canola stubble

2005· article· en· W1979585039 on OpenAlexaffvenue
Peter V. Blenis, Pak S. Chow

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCanolaLeptosphaeria maculansBlacklegBiologyBrassicaAgronomyPathosystemPhomaHorticultureInoculation

Abstract

fetched live from OpenAlex

Abstract The survival and impact on canola of Leptosphaeria maculans, the causal agent of blackleg, might be reduced by agents that decompose canola stubble. Fifty-six fungal isolates from wood or canola were evaluated for their ability to overcome several biological and (or) physical constraints to their effectiveness in decomposing and (or) eliminating L. maculans from canola stubble. Relative to fungi from canola, wood decay fungi were more tolerant of reduced water potential but somewhat less tolerant of lower temperature. Wood decay fungi were no better than those isolated from canola at decomposing sterile canola stubble and less able than Coprinus spp. and Cyathus olla in colonizing and surviving in nonsterile stubble. None of the isolates were effective in eliminating L. maculans from stubble pieces or causing significant decomposition of nonsterile stubble either under laboratory conditions or in the field. These results suggest that considerable effort would be required to find isolates effective in managing blackleg disease through stubble decomposition. Wood decay fungi would seem to have little potential as biological control agents in this pathosystem because of their inability to colonize and decompose nonsterile canola stubble. Keywords: Leptosphaeria maculans blacklegcanoladecomposition

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.042
GPT teacher head0.261
Teacher spread0.219 · 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 designObservational
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

Citations9
Published2005
Admission routes2
Has abstractyes

Explore more

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