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Record W2900737346 · doi:10.1139/cjb-2018-0058

Relationship between alternate bearing and apical bud development in cranberry ( <i>Vaccinium macrocarpon</i> )

2018· article· en· W2900737346 on OpenAlexvenueno aff
Jenny Bolivar-Medina, Juan Zalapa, Amaya Atucha, Sara E. Patterson

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
FundersAgricultural Research ServiceCranberry InstituteU.S. Department of Agriculture
KeywordsBiologyVacciniumBotanyApical dominanceShootHorticultureTraitCultivarGenotypeGeneGenetics

Abstract

fetched live from OpenAlex

Similar to other woody crops, cranberry (Vaccinium macrocarpon Ait.) exhibits alternate bearing or a tendency to produce heavier yields one year, followed by lighter yields the next year. Unfortunately, despite the occurrence in many fruit crops, this trait is not well understood. The variable differentiation of floral initials in cranberry uprights is a distinguishing characteristic associated with alternate bearing. This study evaluates bud morphology and the presence of floral initials through characterization of longitudinal sections of apical buds from vegetative and fruiting uprights of alternate and non-alternate bearing genotypes. Our results reveal that differentiation of floral initials in fruiting uprights only occurs in non-alternate bearing genotypes and after initiation in vegetative uprights. In addition, a strong positive correlation was found between the increase of bud width and the presence of floral initials. Lastly, uprights from the alternate bearing genotype exhibited significantly faster growth rates of the reproductive buds compared with the vegetative buds. In summary, our study shows marked differences in timing and growth rates of floral initials between uprights of the two genotypes, suggesting a possible correlation with resource allocation during the growth season, and thus could contribute to cultivar selection and management practices.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.131

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 studies0.0000.000
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 head0.064
GPT teacher head0.284
Teacher spread0.221 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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