Comparison of Three Nondestructive Methods for Determination of Vegetable Surface Area
Bibliographic record
Abstract
Surface areas of differently shaped vegetables, namely beet ( Beta vulgaris L.), cucumber ( Cucumis sativus L.), carrot ( Daucus carota L.), and parsnip ( Pastinaca sativa L.) were determined by Baugerod's (a linear) method, a shrink-wrap replica method, and image analysis. Values obtained using these methods did not differ significantly for carrots and beets. Surface area values obtained using image analysis were higher than those obtained by Baugerod's method for parsnips (by 23.5%), and higher than Baugerod's and shrink-wrap replica methods for cucumbers (by 11.3% and 12.6%, respectively). A method was considered reproducible if surface area values from five measurements on the same product did not differ significantly ( P ≤ 0.05). Surface area values for an individual product varied in the range of 4.7% for Baugerod's method for parsnips, and 6.6% for the shrink wrap replica method for carrots. No significant variation was observed for any of the vegetables when repeated measurements were made using the image analysis method. Image analysis offers rapidity, lack of adverse effect on produce, and the ability to collect and analyze data simultaneously. However, in absence of the necessary equipment for image analysis, Baugerod's method may be used for a product symmetrical around its central axis, after comparing it with a more direct procedure (e.g., shrink-wrap replica method).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".