Wintering Index and Yield Traits for Early, Mid, and Late Season Strawberry for Colder Climates
Bibliographic record
Abstract
In temperate climatic regions where severe winter conditions persist, the winter hardiness and fruit yield of strawberry cultivars vary greatly in perennial production systems. A 4-year study was carried out to investigate the wintering ability of different June-bearing strawberry cultivars in the cold semi-arid continental climate of Manitoba between 2015 and 2018. The wintering index and yield traits of early, mid, and late season strawberry cultivars were evaluated. Six different cultivars tested in each year. ‘Wendy,’ ‘Kent’, and ‘Cabot’ were common in each year. Mid-season cultivar ‘Kent’ and ‘Mira’ yielded significantly (p< .05) higher with greater wintering index compared to early and late season cultivars within each trial and across 4 years. Low winter temperatures with less snow on grounds establish pronounced effect on early season cultivars wintering index. A significant variation for temperatures was recorded for the months within and between years.
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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".