205 Non-destructive Surface Area Determination of Vegetables
Bibliographic record
Abstract
Surface area of cucumbers, carrots, parsnips, and beets was determined using the following non-destructive methods: Baugerod's method, Baugerod's method with inclusion of a factor correcting for substitution of weight for volume in the formula, and a novel image analysis method. Accuracy of the methods was ascertained by comparison with a direct shrink-wrap replica method of surface area measurement. Vegetables ranged in shape from cylindrical (cucumber and carrot) to conical (parsnip and beet). No difference in accuracy among methods of surface area determination was detected for carrots or beets. Baugerod's method and the image analysis technique differed significantly from the direct shrink-wrap replica technique for surface area determination of parsnips and cucumbers, respectively. Inclusion of a correction factor in Baugerod's method did not increase the accuracy of this method for any of the vegetables. The precision and repeatability of each method was determined by repeated measures analysis. Baugerod's method lost precision and repeatability for the conically shaped vegetables. Conversely, the shrink-wrap replica method lost precision and repeatability for the cylindrically shaped vegetables. The image analysis technique was precise and highly repeatable over the range of vegetable shapes. The development of a rapid, accurate, and precise non-destructive method of surface area measurement using image analysis techniques will provide a useful tool in the physiological study of vegetable products. Applicability of such a method over a range of vegetable shapes will be of additional value.
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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) | 0.001 | 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) | 0.003 | 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".