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Record W2788509815 · doi:10.1139/cjps-2017-0365

Performance of five haskap (Lonicera caerulea L.) cultivars and the effect of hexanal on postharvest quality

2018· article· en· W2788509815 on OpenAlexaffvenueabout
John O. MacKenzie, Evan Elford, Jayasankar Subramanian, Rob William Brandt, Katherine Elizabeth Stone, J. Alan Sullivan

Bibliographic record

VenueCanadian Journal of Plant Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsHexanalPreharvestPostharvestRipeningCultivarHorticultureTitratable acidBiologyAgronomyFood science

Abstract

fetched live from OpenAlex

Growers are challenged to provide premium, ripe haskap fruit to market while limiting the negative attributes associated with over-ripening. Hexanal is an inhibitor of phospholipase D, an enzyme involved in membrane degradation, and has shown promise in extending the longevity of fruit. This study investigated the performance of a hexanal-based preharvest spray for enhancing the quality and shelf life of haskap fruit, an emerging crop in Canada. At two locations in 2015, 2016, and 2017, five cultivars were sprayed with a control treatment or a 0.02% hexanal formulation at 2–3 wk before harvest. Fruit was stored at 4 °C and assessed for quality at successive times. Although inconsistent, results suggest a hexanal spray may impart a small benefit to the postharvest quality of haskap fruit. Secondary objectives included describing important agronomic characteristics of haskap and assessing the potential of a hexanal preharvest spray for enhancing fruit retention. Hexanal did not reduce fruit drop and had no effect on quality at harvest. Fruit contained high levels of soluble solids and titratable acids, were very dark in colour, and did not degrade in quality as rapidly as softer fruit crops such as strawberry or raspberry.

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)0.004
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 score0.224
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.239
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

Citations12
Published2018
Admission routes3
Has abstractyes

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