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Record W2582446665 · doi:10.21273/hortsci.41.4.1059d

(150) Photo, Physical, and Mechanical Pretreatments Reduce Incidence of Blight Diseases in Carrots

2006· article· en· W2582446665 on OpenAlexaff
Kathryn Ruth Pickle, Rajasekaran R. Lada, C. D. Caldwell, Jeffrey Hoyle, Jeffrey Norrie

Bibliographic record

VenueHortScience · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsAcadian Seaplants (Canada)Nova Scotia Department of Agriculture
Fundersnot available
KeywordsBlightAbiotic componentDaucus carotaBiotic componentCanopyBiotic stressResistance (ecology)BiologyHorticultureAbiotic stressBotanyAgronomyToxicologyEcology

Abstract

fetched live from OpenAlex

Consumer demand for chemically free produce has increased; however, producers have become increasingly dependent on unreliable chemical defenses for control of diseases and pests. These dilemmas, along with the desire to maintain healthy farmland, have led to the research and development of environmentally sound practices. It is hypothesized that predisposing plants to photo, physical, and mechanical (PPM) mechanisms can allow plants to better withstand stress. Plants exposed to one form of PPM mechanism could confer resistance to a range of biotic and abiotic stresses. Such cross-resistance is commonly seen, but not well-understood. In this study, various PPM factors, including UV-C radiation, leaf brushing, and canopy trimming, were applied to field-grown carrots ( Daucus carotae L.). The degree of blight and white mold infection was measured. Preliminary analyses showed that UV-C radiation at 4 weeks post-emergence or brushing at 4 or 8 weeks significantly reduced carrot blight and/or white mold. This implies that certain PPM mechanisms may induce plant defenses, allowing the crop to better defend itself against future biotic stress.

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 score0.425
Threshold uncertainty score0.316

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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