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Record W4403575363 · doi:10.48130/frures-0024-0031

An assessment of the flavour quality attributes of Staccato, Sweetheart, and Sentennial sweet cherry cultivars in relation to maturity level at harvest

2024· article· en· W4403575363 on OpenAlexafffund
Kelly Ross, Naomi C. DeLury, Lana Fukumoto, Jillian A. Forsyth

Bibliographic record

VenueFruit Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersBC Cherry Association
KeywordsFlavourCultivarMaturity (psychological)HorticultureRelation (database)Quality (philosophy)BiologyBotanyPsychologyComputer sciencePhysicsFood scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

The present study aimed to: 1) examine indicators of maturity for harvest, and 2) determine whether maturity at harvest affects flavour quality retention. Data was collected over the 2018, 2019, and 2021 growing seasons for three sweet cherry cultivars: Sweetheart, Staccato, and Sentennial. Using the CTIFL (Centre Technique Interprofessionnel des Fruit et Legumes, Paris, France) colour wheel standard, cherries were collected at the 3-4, 4-5, and 5-6 colour levels to obtain cherries at different maturity levels. Assessment of fruit quality was performed on harvest and 28-d stored cherries. The respiratory activity of the cherry cultivars harvested at different colour levels was assessed. Environmental data was also collected over all growing years. Dry matter was a better indicator of flavour quality than colour, as the dry matter was related to both soluble solids, and titratable acidity. Although colour was found to be related to soluble solids, not titratable acidity, this work identified colour was not a reliable indicator of maturity and/or flavor quality as cherries of the same colour may differ in dry matter, soluble solids and titratable acidity due to cultivar and growing condition differences. Sweet cherries may self-actualize when growing conditions are favourable, reaching optimal dry matter levels that indicate maturity, despite their colour, resulting in lower respiration and allowing cherries to retain their flavour quality in storage. As the time it takes cherries to reach self-actualization differs between cultivars and growing years and may be reached at varying colour ranges, optimum dry matter standards should be developed for each different sweet cherry cultivar under different environmental conditions. Under the field conditions experienced in this study, optimal dry matter ranges were established for Sweetheart (22.5%−25%) and Staccato (19.5%−22.5%), while more analysis is required to determine optimal dry matter for Sentennial, dry matter in the range of 20.5% to 22.6% maintained lower respiration rates at lower temperatures, potentially improving the ability to maintain quality after harvest.

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)0.002
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 score0.815
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.234
GPT teacher head0.450
Teacher spread0.216 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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