Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pulses (Fabaceae) have regained interest for their high protein level. However, food application of pulses and pulse ingredients is hampered by several issues around their off‐flavor. Off‐flavors in pulses are partially inherent and partially produced during harvesting, processing, and storage. Generally, volatile off‐flavor compounds in pulses belong to the categories of aldehydes, alcohols, ketones, acids, pyrazines, sulfur compounds, and others, and off‐taste is strongly correlated to the presence of saponins, phenolic compounds, and sometimes alkaloids. No systematic studies have been performed on the identification of the off‐flavor compounds present in pulses in relation to their contribution to the overall perception of the pulses. This review article aims to provide a concise overview highlighting the most important aspects of the knowledge available on the off‐flavor compounds present in various pulses, their possible origins, and the technologies available to prevent, reduce, or mask these off‐flavor compounds. Rather than attempting to make a full inventory of the literature in the field, this paper addresses the most relevant topics referring to a selected set of relevant papers on each topic to substantiate the observations and conclusions that may guide the reader toward additional literature.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it