Foliar Calcium Corrects a Deficiency Causing Green Fruit Drop in ‘Draper’ Highbush Blueberry (Vaccinium corymbosum L.)
Bibliographic record
Abstract
‘Draper’ northern highbush blueberry (Vaccinium corymbosum L.) is a widely-planted mid-season cultivar with excellent fruit quality. Under the climatic conditions of Southwestern British Columbia, Canada, and Northwestern Washington, USA, it expresses a physiological disorder causing spontaneous green fruit drop (GFD) of up to half of the developing crop just prior to onset of the fruit coloring phenophase. Reduction of economic losses due to GFD required identification of the cause of this disorder and development of an agronomic solution that would reduce fruit drop. In 2014, two initial experiments were conducted to compare three foliar Ca products under a range of N fertilization rates. In 2015 and 2016, three locations were used in a first step to optimizing rates and timings of foliar Ca application. Initial experiments determined that higher N fertilization rates exacerbate GFD but that foliar Ca corrects the condition. Multi-site, multi-year trials identified key rates and timings for foliar Ca application to provide an agronomic solution for commercial growers. These trials identified an acute fruit Ca deficiency as the cause of GFD, and that foliar calcium applied frequently at high concentration from mid-bloom onward can be effective in reducing GFD, often to negligible levels. This condition has now been reported in several production regions around the world.
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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 | 0.001 |
| 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".