Calculation of the Thermal Properties (and Their Uncertainties) of Strawberry During Its Cooling Under Natural Convection
Bibliographic record
Abstract
Many times, the thermal properties of a product are determined but their uncertainties (and, mainly, the covariance matrix) are not provided. Thus, in the simulations, it is not possible to establish a confidence band for a transient state described through the values obtained for these properties. In this article, a model was proposed to determine thermal diffusivity and convective heat transfer coefficient, providing the above-mentioned lack of information, for a product with spherical geometry during its cooling. The proposed model involved: 1) an experimental data set of the cooling kinetics in a point within the product; 2) a one-dimensional numerical solution of the heat conduction equation; 3) an optimizer based on the Levenberg-Marquardt algorithm to determine the thermal properties, their uncertainties, and the covariance between the parameters. Model was applied for determining thermal properties of strawberries, using an equivalent sphere to represent the geometry of the product, and the obtained results were compatible with the literature results.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".