A Method for Intercultivar Comparison of Potato Tuber Nutrient Content Using Specific Tissue Weight Proportions
Bibliographic record
Abstract
Potato tubers are a staple food item in the North American diet. Each potato cultivar has unique tuber appearance and nutritional composition. A method was developed to facilitate better cultivar selection for dietary purposes and obtain a better understanding of the nutrient distribution within specific tissues of potato tubers. This involved a procedure for estimating the percent weight contribution of the 3 major tissue components, including periderm or "skin," cortex, and pith for 20 potato cultivars. Weight determination was based on the volume (calculated through an ellipsoid formula) and density of each component tissue. Calculated percent weight and dry matter data for each tuber tissue provided conversion factor values that were tabulated for all cultivars. An example is provided to illustrate the application of this procedure in facilitating identification of cultivars with significantly greater or lesser protein content.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".