Evaluation of the Sensory, Physicochemical, and Visual Characteristics of a Sweet Cherry Cultivar Treated in a Commercial Orchard with a Cherry Cuticle Supplement when a Rainfall Event Does Not Occur
Bibliographic record
Abstract
The splitting of sweet cherry ( Prunus avium L.) just before harvest can be a considerable problem in the Okanagan Valley (BC, Canada). In an attempt to minimize economic losses, growers apply a commercial cherry cuticle supplement in anticipation of a rainfall event. However, it is unknown if this product affects flavor, texture (crispness, firmness, and juiciness), or visual characteristics (stem browning, pitting, and pebbling) of sweet cherry. Therefore, this research was undertaken to evaluate the effects of a cherry cuticle supplement on the sensory, physicochemical, and visual characteristics of ‘Skeena’ sweet cherry. Firmness measurements were assessed with a fruit-firmness tester, whereas sensory determinations were assessed at first bite (whole-cherry crispness) and after multiple chews (flesh firmness) by a panel of 14 trained panelists. Fruit treated with the cherry cuticle supplement had lower instrumental firmness compared with the control, which was most pronounced after 28 days, with a reduction of 0.53 N. Treated fruit also had significantly lower sensory firmness and higher juiciness than the control fruit. Fruit treated with the cherry cuticle supplement had reduced water loss, less pitting, and lower stem-pull force, resulting in higher frequency of detachment of the stems. Further research would be necessary to evaluate the effects with other cultivars, and in years with rainfall events, as well as when hydrocooling is used.
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) | 0.000 | 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 | 0.001 |
| 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".