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Record W2109609216 · doi:10.1002/jsfa.2249

Coliphage as an indicator of fecal contamination in hydroponic cucumber (<i>Cucumis sativus</i> L) greenhouses

2005· article· en· W2109609216 on OpenAlexaff
Jiali Xu, Keith Warriner

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsColiphageCucumisFecal coliformGreenhouseBiologyContaminationHorticultureIndicator bacteriaFecesHydroponicsWater qualityEscherichia coliMicrobiologyBacteriophageEcology

Abstract

fetched live from OpenAlex

Abstract A preliminary study has been performed to evaluate the utility of somatic and F + ‐specific coliphage as quality indicators of irrigation water used in hydroponic cucumber greenhouse operations. Samples of incoming water, waste hydroponic solution, cucumber fruit and plant roots derived from two greenhouses were screened for bacterial fecal indicators (generic Escherichia coli , fecal coliforms, total coliforms and Clostridium perfringens ) and coliphage. Bacterial fecal indicators were present in incoming water and in spent hydroponic solution with coliphage only being sporadically recovered. However, both somatic and F + ‐specific coliphage were consistently recovered from the roots of cucumber plants along with bacterial fecal indicators. Despite the heavy contamination of plant roots, the cucumber fruits were within acceptable microbiological limits. F + ‐specific coliphage was recovered from 1 out of 25 cucumbers tested along with generic E coli . In contrast, no somatic coliphage was recovered from cucumbers despite coliforms being present on 15 out of 25 units. In conclusion, coliphage represents a poor index of hydroponic irrigation water quality but presence on cucumber fruit and roots can be used to highlight the presence of fecal indicator bacteria within greenhouse operations. Copyright © 2005 Society of Chemical Industry

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 score0.189
Threshold uncertainty score0.172

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 communication0.0000.001
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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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