MétaCan
Menu
Back to cohort
Record W2184749456 · doi:10.21273/jashs.126.6.771

Using Volatile Emissions and Chlorophyll Fluorescence as Indicators of Heat Injury in Apples

2001· article· en· W2184749456 on OpenAlexaff
Jun Song, Lihua Fan, Charles F. Forney, Michael Jordan

Bibliographic record

VenueJournal of the American Society for Horticultural Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBrowningFleshChemistryChlorophyll fluorescenceMalusHorticultureChlorophyllCultivarEthanolEthyleneBotanyFood scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

Volatile emissions and chlorophyll fluorescence were investigated as potential signals of heat injury for apple [ Malus sylvestris (L.) Mill. var. domestica (Borkh.) Mansf.] fruit. `McIntosh', `Cortland', `Jonagold', and `Northern Spy' apples were exposed to 46 °C for 0, 4, 8, or 12 hours (heat treatments). Following treatments, fruit were kept at 20 °C and evaluated after 1, 2, 4, or 7 days. Heat treatments induced volatile production including ethanol and ethyl acetate. The 8 and 12 hours heat treatments increased ethanol and ethyl acetate production in all four cultivars by as much as 170- and 11-fold, respectively, 1 day after treatments. Heat treatments also reduced ethylene production and chlorophyll fluorescence. Heat for 12 hours caused serious flesh browning. Among the cultivars investigated, `Northern Spy' and `McIntosh' were most susceptible to heat stress based on the degree of flesh browning. Correlation coefficients of heat stress induced ethanol emission and chlorophyll fluorescence with flesh browning were 0.82 and -0.66, respectively. The nondestructive measurements of ethanol emission and chlorophyll fluorescence have potential to identify stressed fruit with reduced quality or compromised storage life.

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)0.001
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 score0.911
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.301
Teacher spread0.273 · 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

Citations32
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society for Horticultural ScienceSame topicPostharvest Quality and Shelf Life ManagementFrench-language works237,207