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Record W2140546100 · doi:10.21273/hortsci.35.5.894

Objective Method for Measuring Firmness of Iceberg Lettuce

2000· article· en· W2140546100 on OpenAlexaff
R. Andrew Schofield, Jennifer R. DeEll, Dennis P. Murr

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLactucaMathematicsCompression (physics)IcebergCompression testReproducibilityPostharvestSample (material)PopulationStatisticsHorticultureMaterials scienceBiologyComposite materialGeographyChemistryMedicine

Abstract

fetched live from OpenAlex

Traditional hand compression firmness scores of iceberg lettuce ( Lactuca sativa L.) heads were compared with force-deformation data collected from parallel-plate compression tests conducted with a universal testing machine. Sample deformation was measured over a load range of 30 to 40 N. A quadratic response surface best described the relationship between hand firmness scores (1 to 5 scale) and three measurements of sample deformation (mm). Sample deformation was as precise as hand compression in measuring lettuce firmness, and it provided improved reproducibility by eliminating much of the human error. Although adequate for most firm heads, the predictive ability of the statistical model was weak for soft heads (when the hand firmness score was <2), and for heads with inconsistencies in firmness because of uneven leaf distribution. The minimum sample size required to determine accurately the mean firmness score (±0.5 units) of a population of harvested lettuce was ≈20 heads. This may be a disadvantage, since sampling one head requires ≈1.5 minutes. Overall, the instrument-based method measures lettuce firmness as precisely as the hand compression method, and provides a standardized, objective measurement for postharvest researchers when exchanging or reporting firmness data.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.278
Teacher spread0.228 · 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 designOther design
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
Published2000
Admission routes1
Has abstractyes

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