Sweet potato production in a short-season area utilizing black plastic mulch: effects of cultivar, in-row plant spacing, and harvest date on yield parameters
Bibliographic record
Abstract
The sweet potato (Ipomoea batatas (L.) Lam.) requires a long, hot growing season to attain good yields. In a cool climate, the use of black plastic mulch to heat the soil can improve growth but cultivars, plant spacing, and harvest date must be carefully selected to optimize yields and to attain market quality standards. In this two-year study in Quebec, Canada, two sweet potato cultivars (‘Georgia Jet’ and ‘Beauregard’) were grown at four in-row spacings (15, 30, 45, and 60 cm) and harvested at three dates (mid September, late September, and early October). Cumulative growing degree-days (GDD) with base temperatures of 10°C and 15.5°C were calculated for each harvest date. ‘Georgia Jet’ had higher total and marketable yields than ‘Beauregard’. In-row spacing had no effect on yields per hectare of ‘Beauregard’ and only affected ‘Georgia Jet’ in one year of the study. Average root weight of sweet potatoes, yields per plant, and number of roots per plant increased with wider spacing. Delaying harvest by one or two weeks had little effect on ‘Beauregard’ but increased yields of ‘Georgia Jet’. GDD may be a useful predictor of optimum harvest date but a lower base temperature used to calculate GDD may be desirable with ‘Georgia Jet’ as its yields continued to increase even when growing under cool conditions of late September and early October.
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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".