Dry matter partitioning in a nursery and a plasticulture fruit field of strawberry cultivars 'Sweet Charlie' and 'Camarosa' as affected by prohexadione-calcium and partial leaf removal
Bibliographic record
Abstract
Summary Strawberry plants ( Fragaria × ananassa Duch.) are planted in Canadian nurseries in spring to be dug in autumn as bare-root transplants for winter annual plasticulture fruit production in the south-eastern U.S.A.. A series of whole plant harvests were performed on 'Sweet Charlie' and 'Camarosa' strawberry plants in a nursery and a plasticulture fruit field to study their pattern of dry matter partitioning. Plants were either treated with prohexadione-calcium or mowed, or treated with prohexadione-calcium and mowed in the nursery and compared to untreated plants. All treatments caused a reduction in plant height at the time bare-roots transplants were dug in the nursery. Treated plants allocated more dry matter to root and less to leaves, resulting in an increase in root to shoot ratio and this effect lasted until plants were well established after transplantation into the plasticulture system. By fruiting, treated plants allocated more biomass to fruits, and this difference was due to increased fruit number and not increased fruit size. Untreated plants allocated more to leaves, both in number and percentage, and to stems. Prohexadione-calcium increased root allocation, and mowing (alone or combined with prohexadione-calcium) decreased it. Plants that were prohexadione-calcium-treated and mowed had the highest harvest index and untreated plants had the lowest. 'Camarosa' developed many more leaves and proportionally less fruits than 'Sweet Charlie' during the fruiting phase.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".