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Record W2000744910 · doi:10.4141/p05-137

Effect of crop rotation and soil amendments on Sclerotinia stem rot on soybean in two soils

2007· article· en· W2000744910 on OpenAlexvenueaboutno aff
Guillaume Xavier Rousseau, S. Rioux, Daniel Dostaler

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsLoamMonocultureAgronomySclerotinia sclerotiorumCrop rotationCompostSclerotiniaStem rotSoil waterBiologyCropMathematicsEnvironmental scienceHorticultureSoil science

Abstract

fetched live from OpenAlex

The effect of 4-yr soybean rotations with corn (CCCS vs. CCSS vs. SSSS) and soil amendments (urban compost vs. mineral fertilizer) on Sclerotinia stem rot of soybean (disease severity index, apothecia number, sclerotia survival), caused by Sclerotinia sclerotiorum, was studied on two soils (clay loam and sandy loam) at Saint-Hyacinthe, in southwestern Québec, from 1999 to 2002. In 2002, the 3-yr-corn rotation had a significant effect on the disease severity index (DSI) at the clay loam site; DSI was greatly reduced by rotation (5.2%) compared with monoculture (44.3%). At the sandy loam site, an interaction between rotation and fertility amendments reduced DSI from 71.7% in the monoculture to 37.8% in the 3-yr-corn rotation amended with compost. Canonical redundancy analysis (RDA) of the DSI-apothecia matrix in 2002 confirmed the suppressiveness of the 3-yr-corn rotation and revealed a significant conducive effect on DSI of compost at the clay loam site. At the sandy loam site, RDA of the DSI-apothecia matrix confirmed suppressive interaction between corn-soybean rotations and compost amendment. Key words: Sclerotinia stem rot of soybean, crop rotation, urban compost, multiple regression, canonical redundancy analysis, permutations test

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)0.002
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 score0.310
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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

Citations33
Published2007
Admission routes2
Has abstractyes

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