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Record W2543766121 · doi:10.21273/hortsci.35.3.405d

097 Growth Regulator Application Improves Postharvest Quality of Cut Lilies

2000· article· en· W2543766121 on OpenAlexaff
Susan S. Han

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPostharvestGrowth regulatorHorticultureCold storageBiologyRegulatorVase lifePlant growthBotanyCut flowersCultivar

Abstract

fetched live from OpenAlex

Effects of the duration of cold storage, as well as the concentrations, timing, and means of application of a growth regulator solution on the postharvest quality of cut Oriental and Asiatic lilies were evaluated. Without cold storage, lower leaves of Oriental lily `Stargazer' began to turn yellow ≈1 week after placing stem in an interior environment. The development of leaf yellowing continued to progress upward until the end of the vase life when there was an average of >25% chlorotic leaves. Cold storage worsened the leaf disorder. The longer the duration of cold storage, the earlier the development of leaf yellowing and the higher the percentage of leaves that were chlorotic. Spraying leaves with a solution containing 25 mg·L -1 each of BA and GA 4+7 significantly reduced cold-storage-induced leaf yellowing and bud abortion in both Oriental and Asiatic lily. Concentration of the growth regulator solution was inversely related to the development of leaf yellowing. Timing of the growth regulator application was not critical, as there were no differences in leaf yellowing or bud development when the growth regulator solution was sprayed before or after the cold storage. Addition of the growth regulators to the preservative solution completely prevented leaf yellowing but also induced bud abortion. For practical application, spraying growth regulators prior to or after the cold storage would significantly improve the postharvest quality of cut lilies.

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 score0.958
Threshold uncertainty score0.161

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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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