Determination of optimal harvest boundaries for ‘Ambrosia’ apple fruit using a delta-absorbance meter
Bibliographic record
Abstract
In this study, a new chlorophyll measurement tool, the delta absorbance (DA) meter, was used to develop an optimal harvest maturity model for ‘Ambrosia’ apple (Malus × domestica Borkh.) fruit. Fruit from four commercial orchards in the Annapolis Valley, Nova Scotia, Canada, were sampled (25 fruit from three or four trees per location) over nine consecutive weekly harvests during the 2011 and 2012 growing seasons, and 8 weeks in the 2013 season. At each harvest, five fruit from each orchard site had their index of absorbance difference (IAD) values, firmness, mass, titratable acidity (TA), soluble solids content (SSC), red skin colouration and internal core ethylene concentrations measured. Following approx. 3 months of storage at 3.5°C, 20 fruit from each site were removed and assessed for the incidence of disorders such as senescent breakdown, cortical browning and coreflush. Chlorophyll concentrations in the epidermis were strongly and positively related to IAD values in the same tissue (P ≤ 0.001), confirming the assumption that chlorophyll was the basis for the DA meter IAD signal. In addition, IAD values declined significantly during fruit maturity and were negatively related to harvest week (P ≤ 0.001). The optimum harvest period was identified by aligning all ‘at harvest’ IAD values, fruit quality measurements, and ‘post-storage’ disorder data with the corresponding harvest week. IAD values associated with harvests having the highest commercial fruit quality then delineated the optimal harvest boundaries. The upper boundary IAD value of 0.47 was defined as ‘when to begin harvest’, while the lower boundary IAD value of 0.28 was considered to be ‘when to end harvest’ for long-term storage. The use of a DA meter and its IAD value to define the optimal harvest boundaries may be applicable to all commercial apple cultivars, but should be developed for each cultivar and growing region.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".