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Record W4385658560 · doi:10.3390/agronomy13082081

Brewer’s Spent Grain with Yeast Amendment Shows Potential for Anaerobic Soil Disinfestation of Weeds and Pythium irregulare

2023· article· en· W4385658560 on OpenAlexfundno aff
Danyang Liu, Jayesh B. Samtani, Charles S. Johnson, Xuemei Zhang, David M. Butler, Jeffrey F. Derr

Bibliographic record

VenueAgronomy · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureNorth American Strawberry Grower's AssociationU.S. Department of Agriculture
KeywordsBiologyPythiumBranAgronomyWeedFumigationWeed controlHorticultureBotanyRaw material

Abstract

fetched live from OpenAlex

Anaerobic soil disinfestation (ASD) is a promising alternative to chemical fumigation for controlling soilborne plant pathogens and weeds. This study investigated the impact of brewer’s spent grain (BSG), a locally available carbon source, on various weed species and the oomycete pathogen Pythium irregulare in ASD. Two greenhouse studies were conducted using BSG and yeast at full and reduced rates in a completely randomized design with four replicates and two runs per study. In both studies, ASD treatments significantly decreased the seed viability of all weed species and the Pythium irregulare inoculum, while promoting higher cumulative anaerobicity compared to the non-treated control. The addition of yeast had a notable effect when combined with BSG but not with rice bran. When used in reduced carbon rates, yeast supplementation enhanced the efficacy of BSG, providing comparable control to the full rate for most weed species, including redroot pigweed, white clover, and yellow nutsedge. Interestingly, no ASD treatment affected the soil temperature. Furthermore, BSG treatments caused higher concentrations of volatile fatty acids compared to ASD with rice bran and the non-treated control. This finding suggests that the inclusion of yeast in ASD shows potential for reducing the carbon input required for effective soil disinfestation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.147

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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