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Record W3193413263 · doi:10.1139/cjps-2020-0332

Bud dormancy pattern, chilling requirement, and cold hardiness in <i>Vitis vinifera</i> L. ‘Chardonnay’ and ‘Riesling’

2021· article· en· W3193413263 on OpenAlexaffvenueabout
Alireza Rahemi, Helen Fisher, Adam Dale, Toktam Taghavi, J. M. Kelly

Bibliographic record

VenueCanadian Journal of Plant Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHardiness (plants)VineyardDormancyVitis viniferaCultivarHorticulturePhenologyBiologyChilling requirementAnnual growth cycle of grapevinesBotanyGerminationShoot

Abstract

fetched live from OpenAlex

In recent years, new vineyards have been established in southwestern Ontario. The open water of Lake Erie provides some winter protection for Vitis hybrids and winter-hardy Vitis vinifera L. cultivars in this area. However, winter damage is possible when vines are grown distant from the open water or when lakes are frozen. To better understand the risks to winter survival, the dormancy and chilling phenology were studied over three winters from 2013–2016. Ten dormant canes of two V. vinifera cultivars, ‘Chardonnay’ and ‘Riesling’, were collected weekly from 1 September until 30 March from the mature vines in a commercial vineyard located at St. Williams (Ontario). The canes defoliated in early October, and the endodormancy was completed by the end of December. The cumulative chilling hours (0–7.2 °C) from defoliation until the completion of endodormancy were averaged 606 hours for ‘Chardonnay’ and 665 hours for ‘Riesling’. ‘Chardonnay’ buds were slightly less hardy than ‘Riesling’ to cold temperatures, with a threshold of about −24 °C for ‘Chardonnay’ and −25 °C for ‘Riesling’. Most primary buds of both cultivars died after 16 February 2015, and more than half died after 12 February 2014, due to severe low temperatures of −33.1 and −26 °C, respectively.

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)0.001
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 score0.795
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.235
Teacher spread0.205 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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