No rose without a thorn: Hedonic testing of live rose plants
Bibliographic record
Abstract
Abstract Sensory and consumer testing of live rose bushes presents several unique logistical challenges due to product size and the need to present roses during a small window of opportunity when they are in full bloom, the timing of which differs from plant to plant. The current study determined whether online (close up photographs of rose blooms) and in‐person (live plants) liking tests produced comparable results and discusses the logistical considerations of in‐person testing. Three studies were conducted: two in‐person to compare two different study design strategies (n = 199, n = 206) and one online (n = 209). Photos of rose blooms evaluated online did not correlate with in‐person liking evaluations (R2 = .00003). The best approach for in‐person testing (completing testing in 1 week with only blooming roses versus spreading out testing over 3 weeks) depended on the project budget and whether a particular rose of interest needed to be in the sample set. Practical Applications Many consumer studies on nonfoods have used photographs of products to obtain consumer feedback rather than presenting a live prototype as this approach is more resource‐efficient. However, the present study demonstrates that a photograph of rose blooms (as shown in rose catalogues and plant tags) does not yield comparable liking scores to in‐person evaluation. Because rose bushes are large, highly variable and have fine details, it is challenging to evaluate them from photos that fit on a computer screen. The current study also describes a protocol for in‐person consumer hedonic testing of flowering plants. A new experimental design (Sudoku design) is presented, suitable for products that are too large to fit in a sensory booth or otherwise immobile requiring panelists to move around the room from product to product to make their evaluations. Two approaches are discussed which account for the difference in timing of rose blooming across cultivars.
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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 | 0.000 | 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) | 0.006 | 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".