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Record W2342183464

An integrated control of Pythium root rot of greenhouse tomato.

2002· article· en· W2342183464 on OpenAlexaff
Tu Jc

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPythium aphanidermatumPythiumRoot rotTransplantingGreenhouseBiologyHorticultureNutrientZoosporeAgronomyDNS root zonePythium ultimumHydroponicsBiological pest controlBotanySporeSowing
DOInot available

Abstract

fetched live from OpenAlex

Pythium root rot caused by Pythium aphanidermatum is one of the most important diseases of greenhouse tomatoes. Hydroponic culture exacerbates the problem. Both nutrient film technique (NFT) and recirculating growing systems pose a challenge in the control of this disease, because the pathogen, especially the zoospores, can spread easily in the recirculating solution to the whole growing system. Fortunately, hydroponically grown plants are easier to manipulate than soil grown plants, proper manipulation of root environments can lead to excellent disease control. This paper reports the development of an effective integrated control measure for pythium root rot of tomato by integrating pH, bioagent, and ultra-violet irradiation in a specific manner. This integrated control consists of three operations: a) before transplanting, the UV system is connected to sterilize the recirculating solution using 100 mJcm-2; b) after transplanting, the nutrient solution is delivered at pH 5.0 regime for five weeks followed by adjusting pH to 5.8 to 6.2 regime for one week; and c) bacterial bioagent, such as Pseudomonas is introduced into the root zone at 100 mL per plant at 10(8) bacteria mL-1 or added to the nutrient solution to arrive at 10(6) bacteria mL-1 in the solution. This report also discusses the advantages and limitations of this measure in the control of pythium root rot.

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 score0.938
Threshold uncertainty score0.121

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 head0.018
GPT teacher head0.174
Teacher spread0.156 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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