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Aqueous ozone can extend vase-life in cut rose

2009· article· en· W294671457 on OpenAlexaff
Susan G. Robinson, Thomas Graham, Michael Dixon, Youbin Zheng

Bibliographic record

VenueThe Journal of Horticultural Science and Biotechnology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVase lifeOzoneCut flowersAqueous solutionChemistryHorticultureXylemShelf lifeVaseBotanyBiologyFood scienceCultivar

Abstract

fetched live from OpenAlex

SummaryIn order to quantify the shelf-life response of cut roses when stored in aqueous ozone solutions, cut ‘Pascha’ roses were stored in either de-ionised water or aqueous ozone solutions containing an initial dissolved ozone residual of 5.5 mg l−1. The results showed that storing cut roses in aqueous ozone solutions (5.5 mg l−1; renewed daily) can extend vase-life approx. three-fold, from 5 d to 13 d, with a corresponding improvement in their aesthetic appearance throughout the vase-life of the cut rose stem. Results suggest that vase-life improvements are achieved through a reduction in bacterial populations present in the storage solution. Bacteria accumulate on the cut surface of the stems, thereby reducing their water uptake capacity. Microbial accumulation was reduced by 1.15 log10 CFU g−1 FW when stems were stored in holding solutions containing 5.5 mg l−1 dissolved ozone, with a corresponding increase in water uptake. Roses stored in ozonated water exhibited higher numbers of functional xylem vessels, water uptake, relative water content, relative fresh weight, acid fuchsin uptake rate, leaf stomatal conductance, and net CO2 assimilation rate, compared to those stored in de-ionised water. The results suggest that ozone can extend cut rose vase-life.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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