THERMO‐PHYSICAL PROPERTIES OF SELECTED VEGETABLES AS INFLUENCED BY TEMPERATURE AND MOISTURE CONTENT
Bibliographic record
Abstract
ABSTRACT Thermo‐physical properties of selected vegetables (cassava, eggplant, ginger, green pepper, white radish and zucchini) were evaluated under different conditions of temperature (5 to 40C) and moisture content (30 to 94% wb). Test samples were equilibrated to a given temperature and moisture content prior to use. The thermal conductivity and heat capacity were measured using the line heat source probe and the differential scanning calorimeter, respectively. Depending on the state, temperature, moisture content and the type of vegetable, average heat capacity varied from 1.5 to 4 kJ/kgC, while the thermal conductivity varied from 0.08 to 0.60 W/mC. Heat capacity of the unfrozen vegetables was found to be approximately twice as much as values obtained for the frozen ones. Although both temperature and moisture affected the thermal conductivity, the latter had the greater influence. Generally, differences in measured data and responses to both temperature and moisture content were attributed to differences in structural characteristics and composition. Correlations were developed for estimating thermo‐physical properties of several vegetables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".