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Record W2404135380 · doi:10.1139/cjps-2015-0257

Abscisic acid form, concentration, and application timing influence phenology and bud cold hardiness in Merlot grapevines

2016· article· en· W2404135380 on OpenAlexaffvenueabout
Pat Bowen, Krista C. Shellie, Lynn J. Mills, Jim Willwerth, Carl Bogdanoff, Markus Keller

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsBrock UniversityWind Energy Institute of Canada
FundersAgricultural Research ServiceU.S. Department of Agriculture
KeywordsHardiness (plants)Abscisic acidAnnual growth cycle of grapevinesPhenologyVeraisonFrost (temperature)PostharvestHorticultureChilling requirementAbscissionBiologyCanopyBotanyBerryDormancyCultivarShootGerminationGeography

Abstract

fetched live from OpenAlex

The effects of abscisic acid (ABA) form, concentration, and application timing on bud cold hardiness, phenology, and fruiting performance of Merlot grapevines (Vitis vinifera) were evaluated in a three-year field trial with site locations in British Columbia and Ontario, Canada, and in Washington and Idaho, United States. Solutions containing natural S-ABA (ABA N ) and a purported long-lived ABA analogue 8'-acetylene ABA (ABA A ) at differing concentrations were applied to the vine canopy at veraison or post-harvest. Postharvest foliar applications of ABA N at concentrations greater than or equal to 5000 ppm tended to advance leaf abscission and increase autumn bud cold hardiness. Postharvest foliar applications of ABA A at 1000 ppm tended to delay budbreak in the spring following application and increase spring bud cold hardiness. Fruit yield and basic composition were affected little by the ABA treatments. Bud hardiness was enhanced by ABA N mostly in autumn and by ABA A mostly in the spring, indicating that the suitability of ABA forms for reducing bud damage would depend on when injurious cold events are more likely.

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 score0.910
Threshold uncertainty score0.982

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 studies0.0000.001
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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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

Citations15
Published2016
Admission routes3
Has abstractyes

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