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Record W2176148793 · doi:10.5073/vitis.2015.54.189-193

Crop load and harvest date have minimal impact on bud cold hardiness and cane carbohydrate levels of four grapevine cultivars

2015· article· en· W2176148793 on OpenAlexaff
René Lefebvre, Andrew G. Reynolds, F. Diprofio

Bibliographic record

VenueFederal Research Centre for Cultivated Plants (Julius Kühn-Institut) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsBrock University
Fundersnot available
KeywordsCultivarHardiness (plants)CaneCropHorticultureBiologyAnnual growth cycle of grapevinesGrowing seasonAgronomyBotanyShootSugarFood science

Abstract

fetched live from OpenAlex

Four grapevine cultivars ('Pinot gris', 'Riesling', 'Cabernet franc', 'Cabernet Sauvignon') were subjected to six different field treatments in 2011 [two crop loads (full, half) X three harvest dates [normal (T0), 3 weeks after T0, 6 weeks after T0] in a randomized block design with a factorialized treatment arrangement. All treatments were sampled four times over the 2012 dormant season from January to March. Bud cold hardiness was evaluated for all four cultivars by measuring low temperature exotherms (LTEs) of dormant buds using differential thermal analysis. Cane carbohydrates (CHOs) were likewise analyzed in 'Pinot gris' and 'Riesling'. CHO analysis was done using an 80 % ethanol extraction and HPLC. Neither CHO levels nor cold hardiness were substantially affected by either crop level or harvest date. Consistent patterns of CHO changes and LTE values in each cultivar indicated that deacclimation was unaffected by treatment. Cold hardiness may be influenced more by cultivar specificity based on rates of maturation than by treatment.

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)0.001
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 score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.348
Teacher spread0.214 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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