MétaCan
Menu
Back to cohort
Record W4320481254 · doi:10.5539/jfr.v12n2p1

Shelf Life of Aquaponically-grown Finstar Lettuce in Different Oxygen Transmission Rate Films

2023· article· en· W4320481254 on OpenAlexvenueno aff
Katherine M. White, J.K. Northcutt, Lance Beecher, Paul Dawson

Bibliographic record

VenueJournal of Food Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
FundersClemson University
KeywordsShelf lifeCarbon dioxideOxygenFood scienceChemistryAnimal scienceWeight lossModified atmosphereHorticultureBiology

Abstract

fetched live from OpenAlex

The effects of oxygen transmission rate of packaging material on the shelf life of aquaponically-grown Finstar lettuce was studied. Parameters of packaging headspace gas composition (oxygen and carbon dioxide concentrations), lettuce pH, percentage weight loss, total aerobic microorganisms, and color were analyzed every ten days for sixty days. Finstar lettuce was stored at 4○C in four different types of packages (treatments), including a clamshell package and three film bags with oxygen transmission rates (OTR) of 3.0-6.0 cc/(m2/24 hr/1 atm), 80-90 cc/(m2/24 hr/1 atm), and >225 cc/(m2/24 hr/1 atm). The percentage weight loss of the 3.0-6.0 OTR package (-0.76-1.05%) was lowest while the percentage weight loss of the clamshell package was highest (0.81-7.72%) among packaging treatments. Nearly ½ of the panelists rated lettuce as fresh enough to eat as is after 50 days of storage in 80-90 cc/(m2/24 hr/1 atm) films while lettuce packaged in the other treatments had less that 1/3 of the panelists judging the lettuce fresh enogh to eat as is. The long shelf-life may be attributed to Finstar having resilient genetic properties along with being greenhouse-grown which lessens the possibility of contamination compared to field-grown lettuces.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.098
GPT teacher head0.331
Teacher spread0.233 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicInnovations in Aquaponics and Hydroponics SystemsFrench-language works237,207