Consumer research explores acceptability of a new Canadian apple – Salish™
Bibliographic record
Abstract
Cliff, M. A., Stanich, K. and Hampson, C. 2014. Consumer research explores acceptability of a new Canadian apple – Salish™. Can. J. Plant Sci. 94: 99–108. This research compared consumer preferences for a new Canadian apple, Salish™ (cultivar SPA493), with commercial cultivars using data collected at two University of British Columbia Apple Festivals (2008, 2010). Mean acceptability by mouth and visual acceptability scores for Salish™ were compared with those for each of three tart (subacid/acid) cultivars (Granny Smith, McIntosh, Spartan) (2008, n ≈ 165) and two relatively new cultivars (Ambrosia, Honeycrisp) (2010, n=1182). T-tests on the 2008 data (n ≈ 165) revealed that Salish™ had higher consumer acceptability than Granny Smith and McIntosh. Analysis of variance of the 2010 data evaluated the influence of ethnicity (ancestral origin), age, gender and stated apple preference (sweet, tart), for the two largest sub-groups of consumers (Asian ethnicity, n=353 European ethnicity, n=725). While 88% of consumers of Asian ethnicity categorized themselves as sweet apple eaters, consumers of European ethnicity were both sweet (55%) and tart (45%) apple eaters. The sweet apple eaters rated Ambrosia higher in acceptability by mouth than Salish™, while tart apple eaters preferred Salish™. On average, consumers’ stated apple preference (sweet, tart) was consistent with their acceptability scores. Mean acceptability by mouth scores for consumers of Asian ethnicity were higher for Ambrosia and lower for Salish™ as compared with consumers of European ethnicity. The visual acceptability of red apples with a green ground (background) colour was significantly lower than those with a yellow ground colour. This suggested that sweet apple eaters of both ethnicities had a strong negative bias for apples with a slightly green ground colour. The work will assist industry in releasing and appropriately marketing cultivars to selected consumers in the metropolitan marketplace.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".