Traditional Curry Pastes During Sukhothai to Ratthanakosin: The Subjective Experience of the Past and Present
Bibliographic record
Abstract
Although there are numerous studies about the traditional curry pastes and Thai food uses claims about the historical development can be divided into nine eras. The research has explored traditional curry pastes’ perceptions of such features and how these might relate to religion and a royal court. Perceptions of curry pastes are influenced by individual and societal factors, local raw materials, and raw materials along the trade routes. Thai food has been influenced by Indian curry and Chinese stir frying techniques. In fact, traditional curry pastes and Thai food has its own culinary style. The traditional curry paste in Sukhothai to Ratthanakosin is heavily influenced continued by religion and a royal court. Traditional curry paste usually contains fresh light flavors of lemongrass and kaffir lime skin and the soothing effect of coconut cream and coconut milk. The spice ingredients are used in curry paste may differ from home to home or region to region. The delicious spicy and hot Thai foods with a traditional blend of aromatic flavors are popular in the world. Usually Thai people eat three times a day includes meat, salads, soup, noodles, curry, and rice. All the recipes are in the collection of original Thai dishes such as a popular curry and also other curries traditionally processed Thai curry paste products named massaman curry called gaeng massaman and well known of hot and sour soup called tom yum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".