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Record W3212966144 · doi:10.1139/cjps-2021-0167

Cold hardiness of select apple cider cultivars in Canada

2021· article· en· W3212966144 on OpenAlexaffvenueabout
John A. Cline, Amanda Beneff, A. Michelle Edwards

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)CultivarHorticultureShootBrowningSeedlingGrowing seasonBiologyBotany

Abstract

fetched live from OpenAlex

There is increasing interest in growing European origin apple cultivars for the production of hard cider in Canada; however, little is known about their winter hardiness. Eleven promising cider cultivars were evaluated for cold hardiness over two consecutive winters and compared with the winter tender cultivar ‘Golden Delicious’. Sections of the current season’s dormant shoots were frozen in a series of test temperatures ranging from −20 °C to −40 °C in a programmable freezer. Xylem tissue browning ratings were used to assess injury after thawing. The temperature of incipient damage (TID), the warmest temperature at which 1-yr-old shoot segments begin to show injury, was obtained from tissue browning curves using non-linear regression. TID varied significantly among cultivars and between sampling years. Overall, the cultivars could be classified according to relative winter hardiness as follows: Ashmead’s Kernel, Bramley’s Seedling (very tender) < Calville Blanc d’Hiver, Porter’s Perfection, Bulmer’s Norman (intermediate) < Crimson Crisp, GoldRush, Golden Delicious, Enterprise, Yarlington Mill, Enterprise (hardy) < Golden Russet (hardy). These data indicate nearly a 10 °C range in winter hardiness amongst the 11 cultivars studied, depending on the sampling date. Ashmead’s Kernel and Bramley’s Seedling appear to be particularly winter tender, whereas Bulmer’s Norman, Porter’s Perfection, and Calville Blanc d’Hiver demonstrated less hardiness during three of the four sampling dates. Based upon these findings, it would be prudent to consult long-term climate normals and consider the frequency of extreme weather events for potential susceptibility to winter injury, particularly prior to establishing more injury-prone cultivars.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.220
Teacher spread0.197 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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