Sauces, spices, and condiments: definitions, potential benefits, consumption patterns, and global markets
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
Spices and condiments are an important part of human history and nutrition, and have played an important role in the development of most cultures around the world. According to the Codex Alimentarius, the category of salts, spices, soups, sauces, salads, and protein products includes substances added to foods to enhance aroma and taste. Spices have been reported to have health benefits as antioxidant, antibiotic, antiviral, anticoagulant, anticarcinogenic, and anti-inflammatory agents. Health claims about the benefits of condiments for disease prevention or health improvement need to be science based and extensively supported by evidence; data on their preventive or protective potential in humans are currently limited. The condiments market has been growing continuously over the last few years, with the quantity of products sold under the category of sauces, dressings, and condiments during the period 2008-2013 increasing from 31,749,000 to 35,795,000 metric tons. About 50 of the 86 spices produced in the world are grown in India. From 2008 to 2013, the United States was the largest importer of spices, followed by Australia, the United Kingdom, Canada, and Russia. The main buyers of fish sauce are Vietnam and Thailand, with purchases of 333,000 and 284,000 metric tons in 2013, respectively. The sauces and condiments category is dynamic, with large differences in consumption in habits and practices among countries. This paper aims to establish definitions and discuss potential health benefits, consumption patterns, and global markets for sauces, spices, and condiments.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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