Determination of optimal harvest boundaries for Honeycrisp™ fruit using a new chlorophyll meter
Bibliographic record
Abstract
DeLong, J., Prange, R., Harrison, P., Nichols, D. and Wright, H. 2014. Determination of optimal harvest boundaries for Honeycrisp™ fruit using a new chlorophyll meter. Can. J. Plant Sci. 94: 361-369. In this study, a new chlorophyll measurement tool [the delta absorbance (DA) meter] was used to develop an optimal harvest maturity model for Honeycrisp™ fruit. Apples from nine commercial orchards in the Annapolis Valley, Nova Scotia, Canada, were sampled over 11 consecutive weekly harvests during the 2010, 2011 and 2012 growing seasons. At each harvest, a sample of fruit was measured for its DA (IAD) values, firmness, titratable acidity (TA),% soluble solids content (SSC), red skin coloration and internal core ethylene. Following approximately 3 mo of storage at 3.5°C, samples were removed and assessed for disorder incidence. The optimal harvest period was identified by aligning all “at harvest” IAD values, fruit quality measurements and “post-storage” disorder data with the corresponding harvest week. Then, the IAD values associated with the harvests having high commercial fruit quality and the least collective expression of disorders, delineated the optimal harvest boundaries. As IAD units declined during fruit maturity, the upper boundary value of 0.59 was deemed “when to begin” harvest, while the lower boundary value of 0.36 was deemed “when to end” harvest for long-term storage. The use of the DA model approach for optimal harvest delineation is potentially applicable to all commercial apple cultivars, but should be developed for each within a distinct growing region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".