The Effect of Seed‐Tuber Physiological Age and Cultivar on Early Potato Production
Bibliographic record
Abstract
Abstract Enhancing physiological aging of seed‐potatoes has the potential to substantially affect production, especially for short‐season growing areas. This study analysed the effect of seed‐tuber age and cultivar, jointly, to identify the combination for optimum early crop production, based on field experiments conducted at two locations in Nova Scotia, Canada. The potato cultivars Superior, AC‐Novachip, Niska, and Yukon Gold were aged by exposing seeds stored at 4 °C to warming periods of 0, 242 (3 weeks), and 484 (6 weeks) day‐degrees, prior to planting. Cultivar and age levels were completely randomized within each location and replicated four times. Harvest periods at 65, 80 and 95 days after planting (DAP) were analysed as an unbalanced split‐plot factorial, with year as a random blocking factor, location as a whole plot treatment, and cultivar and age crossed as subplot treatments. Total yield and marketable yield from Yukon Gold improved with physiological age when harvested early at 65 and 80 DAP, while that from AC‐Novachip improved when aged only 3 weeks. Niska was not affected by age. Overall, AC‐Novachip was consistently better than the remaining cultivars, regardless of age. The best treatment combination that maximized marketable yield was Yukon Gold aged 6 weeks planted at a commercial farm and harvested 95 DAP. For early harvest (65 DAP), however, AC‐Novachip aged 3 weeks and Yukon Gold aged 6 weeks generated the highest yields. Niska and Superior generated lower yield and higher culls.
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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 | 0.000 | 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".