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Record W2377027817 · doi:10.21273/hortsci.49.6.750

Impact of Cluster Thinning and Basal Leaf Removal on Fruit Quality of Cabernet Franc (Vitis vinifera L.) Grapevines Grown in Cool Climate Conditions

2014· article· en· W2377027817 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHortScience · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of British Columbia
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsThinningBerryVineyardTitratable acidAnthocyaninHorticultureVinePruningPhenologyVeraisonBiologyCropBotanyAgronomyEcology

Abstract

fetched live from OpenAlex

Achieving desired fruit quality at harvest in cool climate conditions is a challenge, especially for red varieties, and the typical inability of fruit to reach technological maturity is a critical contributing factor requiring examination. To probe this issue, this research investigated the impact of two levels of crop thinning and of basal leaf removal at three phenological stages in the 2011 and 2012 growing seasons in Michigan. Experiments were conducted at the Southwest Michigan Research and Extension Center (SWMREC) in Benton Harbor. Using ‘Cabernet franc’ ( Vitis vinifera L.) vines, yield components (yield per vine, pruning weight, and cluster and berry weight) and basic fruit composition traits [total soluble solids (TSS), pH, titratable acidity, anthocyanins, and phenolics) were studied to investigate the effect of cluster thinning and basal leaf removal on vine performance and fruit quality at harvest. Neither of the treatments significantly impacted TSS in either of the two seasons. Cluster thinning treatment successfully altered cropload ratio, indexed as Ravaz Index (RI), independently of the time of application. Basal leaf removal increased exposed berry temperature, cluster light exposure, and subsequent anthocyanin and phenolic content of the berry in both seasons, again independent of application date, whereas cluster thinning was effective only in 2012. Crop thinning coupled with basal leaf removal resulted in an increased efficiency in anthocyanin accumulation in relation to TSS accumulation, expressed as anthocyanin:sugar, in both years. This is significant because it offers potential for vineyard management practices aiming to improve fruit quality in cool climates where the onset of anthocyanin accumulation could be reduced and decoupled from sugar accumulation.

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.

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 score0.802
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.334
Teacher spread0.294 · 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