Sugar beet (<i>Beta vulgaris</i> L.) storage quality in large outdoor piles is impacted by pile management but not by nitrogen fertilizer or cultivar
Bibliographic record
Abstract
Van Eerd, L. L., Congreves, K. A. and Zandstra, J. W. 2012. Sugar beet (Beta vulgarisL.) storage quality in large outdoor piles is impacted by pile management but not by nitrogen fertilizer or cultivar. Can. J. Plant Sci. 92: 129–139. Even though storage results in lower sucrose recovery from sugar beets, physical constraints dictate that a significant proportion of the sugar beet crop can be stored up to 120 d before processing. From 2006 to 2010, N fertilization (0–220 kg N ha–1), sugar beet cultivar, and pile management method were independently evaluated to determine their effects on sugar beet storability in large outdoor piles. At harvest, five representative sugar beet samples from the N and cultivar field trials were placed in a large outdoor storage pile. Sugar beet quality assessments were taken at harvest and three times over the storage season. On the last retrieval date only, sugar beet samples were retrieved from piles managed via the length- vs. end-removal method. Although there were differences among N treatments and cultivars in sugar beet quality at harvest, there were no storage date by N treatment or storage date by cultivar interactions for any parameters measured indicating that N fertilization or cultivar did not influence the ability to maintain sugar beet quality in large outdoor piles. The length-removal method of pile management had better quality sugar beets compared with the standard end-removal method. Hence, sugar beet producers do not need to modify production practices to optimize storability, but sugar beet processors can improve sucrose recovery by removing sugar beets lengthwise along both sides of large piles as opposed to the standard end-removal 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".