Development of an objective freshness index for a variety of Mediterranean eggplant
Bibliographic record
Abstract
The objective of this work was to develop a non-subjective freshness index (If) for a Mediterranean variety of eggplant (Solanum melongena L. cv. Traviata). Eggplants were obtained from local agricultural sources and stored under controlled environment conditions. Storage temperature was und to have an effect on If, while the agricultural source and the degree of exposure to light did not affect If. Nonlinear functions modelled If against weight loss (WL), peel gloss loss (GL), surface stiffness loss (SL), density ratio minus one (DRMO), and storage period (in h). The best model for predicting If of eggplant was a function of SL (R2adj =0.98). Stepwise regression identified hyperspectral wavelengths that will predict If at 10°C (R2adj =0.46) and 27°C (R2adj =0.78). If can be used to estimate eggplant quality, therefore future work will develop freshness indices for other eggplant varieties stored under a range of temperature conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".