Determination of optimal harvest boundaries for Honeycrisp™ fruit using a new chlorophyll meter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it