Scheduling adequate irrigation mitigates postharvest soft scald disorder of Ambrosia™ apples grown in a semiarid eco-zone
Bibliographic record
Abstract
The effect of irrigation on soft scald (SS) disorder in Ambrosia™ apples was surveyed over 4 years in various orchards in Cawston, BC which is located in a valley having a semiarid eco-zone. The observations were further validated by manipulating irrigation programs in a series of experiments in three commercial orchards. Adequate irrigation (AI) was defined as the amount of water application required to maintain sustainable production as defined in the provincial irrigation guide, while deficit irrigation (DI) reduced irrigation to less than 40% of AI at the same site. Records from the survey study indicated that SS incidence was negatively correlated with the amount of watering ( r = −0.9). The validation study confirmed that correlation at three different commercial sites. These results suggest that intensive water deficit can cause fruit to be susceptible to SS and that adequate watering during fruit expansion and late season exerts a mitigating effect on SS in Ambrosia™ apples grown in a dry climate region. They also suggest that conducting DI prior to midsummer does not irrevocably cause SS susceptibility in this apple.
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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) | 0.000 | 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".