Application of CaCl2 Sprays Earlier in the Season May Reduce Bitter Pit Incidence in 'Braeburn' Apple
Bibliographic record
Abstract
Calcium application trials were undertaken in a 'Braeburn' apple ( Malus × domestica Borkh.) orchard with a history of bitter pit development at harvest. In 2000, an early season calcium chloride application strategy was compared with the unsprayed control and a late season application strategy. From 2001–03, the assessment of timing of calcium chloride sprays was extended by comparing effects of five weekly sprays applied during the growing season either early, middle, or late season. Other Ca application strategies tested included sprays of acidified calcium carbonate suspensions and soil application of calcium thiosulphate. In the first experiment, early application of calcium chloride reduced the occurrence of bitter pit at harvest and after 3 months cold air storage, despite having low harvest fruit Ca concentrations. Late sprayed fruit had a higher incidence of bitter pit. In the second experiment, the later calcium chloride was sprayed in the growing season, the higher the fruit Ca concentration at harvest. Despite this, no bitter pit was measured at harvest for 2 years for early and midseason calcium chloride spray regimes. In 2003, when Ca disorders were severe and fruit large, bitter pit was observed despite early season calcium chloride sprays. Soil calcium thiosulphate application and foliar sprays of acidified calcium carbonate suspensions failed to meaningfully augment harvest fruit Ca concentrations and affect bitter pit incidence.
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) | 0.001 | 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".