Chefs Evaluate Syrup from the Wild Bigleaf Maple (Acer macrophyllum): A New Artisanal Food from Pacific Northwest Forests
Bibliographic record
Abstract
This paper examines a new artisanal food product made from bigleaf maple trees (Acer macrophyllum). These trees are indigenous to, and thrive in, the moist and mild climate of the Pacific Northwestern regions of the United States and Canada. Small producers have a new artisanal product from this historically valuable tree. The sap is collected from family-owned woodland trees to produce maple syrup. To better understand the gastronomic potential of bigleaf maple syrup, we conducted sensory evaluations with culinary professionals. In this study undertaken in Oregon, the syrup from three small independent producers was subjected to sensory hedonic and attribute assessments by 62 chefs. The results of the sensory assessments and the details of the testing methods and analysis are presented. This paper examines the agreement among the chefs and the implications for the culinary acceptance of this new artisanal product. We discuss in detail (1) the complexity of flavor; (2) the most likely use of bigleaf maple syrup in various foods or as a sweetener; (3) and the four most important reasons for purchasing bigleaf maple syrup consisting of both environmental and taste attributes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".