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Record W2552981906 · doi:10.21273/hortsci11179-16

Diffuse Flesh Browning in ‘Honeycrisp’ Apple Fruit is Associated with Low Temperatures during Fruit Growth

2016· article· en· W2552981906 on OpenAlexaffabout
Cindy B. S. Tong, Hsueh‐Yuan Chang, Jennifer K. Boldt, B. Yizhou, Jennifer R. DeEll, Renae E. Moran, Gaétan Bourgeois, D. Plouffe

Bibliographic record

VenueHortScience · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural Affairs
FundersMaine Agricultural and Forest Experiment StationUniversity of Minnesota
KeywordsBrowningFleshMalusHorticultureBiologyFruit treeRosaceaeChilling requirementBotanyDormancyGermination

Abstract

fetched live from OpenAlex

Multiple types of flesh browning can occur as storage disorders in ‘Honeycrisp’ apple ( Malus × domestica Borkh.) fruit. Predicting its occurrence is hindered by differing definitions of the types of browning, incomplete understanding of their etiologies, and difficulty in assessing harvest maturity of ‘Honeycrisp’ fruit. In 2013, of ‘Honeycrisp’ fruit grown, harvested over multiple weeks, and stored in Maine, Minnesota, Ontario, and Quebec, only the Quebec fruit developed diffuse flesh browning. A detailed comparison showed that the Quebec fruit differed in size, but not in other quality attributes, from fruit of the other locations. The Quebec fruit experienced lower temperatures during active fruit growth and were increasing in cell size up to harvest. Analyses of climate data from 2009 to 2015 indicated that accumulated growing degree-days (GDD) 50–60 day after full bloom (DAFB) could account for 31% of the variation in diffuse flesh browning, and seasonal GDD <500 are associated with a greater likelihood of injury. Fruit that exhibited diffuse flesh browning had higher magnesium and lower fructose levels than unaffected fruit. As these measurements were made after browning was assessed, the timing of the onset of these characteristics in relation to browning cannot be determined.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.310

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 head0.012
GPT teacher head0.196
Teacher spread0.185 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

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