The Effect of Field Heat Reduction Methods on Fresh and Processing Qualities of Red and Russet Potato Cultivars
Bibliographic record
Abstract
The effect of three field heat reduction methods, including temperature lowering stepwise (TLS), temperature lowering gradually (TLG), and temperature lowering immediately (TLI), after harvesting on the quality of Russet Norkotah 3 and red skin numbered line CO 07102-1R potatoes were investigated. The tubers were analyzed at harvest (0 time), when they reached 3 °C, and after 6 months of storage at 3 °C for physiological weight loss, firmness, wound healing, total phenolics content, reducing sugars, and color of french fries. The results indicated that weight and firmness losses were lower under the TLS reduction method after 6 months of storage than TLG and TLI methods. The weight loss in TLS is 12%, TLG 14% and TLI 17% in CO 07102-1R whereas Russet Norkotah TLS is 4%, TLG 6%, and TLI 8% respectively. Wound healing was more effective using the TLS reduction method, especially in Russet Norkotah 3. French fry color was lighter at harvest (USDA grade 0), while there were no differences in the color of French fries prepared from tubers stored under the TLS and TLG methods (both had USDA grade 2). French fries were darker for tubers from the TLI method (USDA grade 4). The total phenolic content and reducing sugars were significantly increased in the TLI field heat reduction method. Keywords: Potato, Storage management, Wound healing, Weight loss, French fry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".