Sensory characterization of conifer-based extracts for culinary uses
Bibliographic record
Abstract
Quebec's forests cover more than half of its territory and offer a great diversity of wild edible products. Culinary professionals are becoming more and more interested in using conifer products as aromatics, but the sensory properties and culinary functionalities of these products are not yet fully known. The objective of this work was to study the sensory potential of conifer needles used as aromatics in cooking and to generate sensory descriptors for these resources. Extracts were prepared in a water-based (maceration, decoction) or oil-based (sous-vide) medium from buds or mature needles of balsam fir, black spruce or white spruce. Projective mapping combined with ultra-flash profiling was performed on 14 conifer extracts with culinary professionals as panelists. The consensus map of descriptors for these conifer extracts revealed a great diversity of smells and aromas. The panelists were able to differentiate the extracts and generated a wide range of sensory descriptors. Some of those descriptors were related to preparation variables. Each extract was chosen at least once as having the best culinary potential. Overall, the extracts have inspired a multitude of uses for conifers as aromatics in cooking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".