Participatory plant breeding reveals that geosmin concentration is not the central determinant of hedonic liking in table beet
Bibliographic record
Abstract
Abstract Participatory plant breeding and rapid sensory evaluation are effective techniques for organic cultivar development. Table beet is an important crop for organic growers, and geosmin, a volatile compound which confers earthy aroma, has been suggested as the attribute around which hedonic liking of beet is organized. Open pollinated table beet populations with diverse pigmentation and low (LGC) or high (HGC) geosmin concentration served as starting materials for the first PPB effort in table beet. This project sought to develop consumer-accepted specialty beet cultivars for organic systems and to gauge consumer perception of and preference for geosmin concentration in non-laboratory conditions. LGC and HGC initial populations were significantly different in mean geosmin concentration but not mean TDS. LGC populations diverged significantly in geosmin concentration over two cycles of selection for hedonic liking, due to drift rather than selection. PPB yielded cultivars ‘Evansville Ember’, ‘Snowglobe’, ‘Blushing Not Bashful’, ‘Evansville Orbit’, and ‘Moving Target’. Cultivar novelty and market development were strengthened by chef input and association with a publicly funded seed system development group. Geosmin concentration was not the central determinant of hedonic liking or perceived earthy flavor in table beet. Earthiness was inconsistently associated with geosmin concentration and hedonic liking. Sweetness and bitterness were positively and negatively correlated with liking, respectively, although sweetness was not associated with variation in TDS. Cultivars with a broad range of geosmin concentration were well accepted by consumers, and manipulating expectation—via appearance—may be as powerful as manipulating flavor compounds in influencing liking of table beet.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".