Back to the Future:Knowledge Light Case Base Cookery
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
Abstract. The domain of cookery has been of interest for Case-Based Reasoning (CBR) research for many years since the CHEF case-based planning system in the mid 1980s. This paper returns to look at this domain, emphasising a knowledge-light approach. Our approach focuses on; the design of a structured case representation which encapsulates the details of a recipe, on leveraging WordNet for identifying food items and the relationships between them, and on using Active Learning to assist in labelling recipes with meal and cuisine types. Users can search for recipes by specifying the ingredients they wish to include in, or exclude from, the recipe and optionally specifying the type of meal and/or cui-sine they are interested in. Recipes are retrieved based on a weighted similarity of the ingredients, the meal and/or cuisine types (if specified) and the textual similarity between the query and specific fields of the recipe text. The system includes substitution adaptation where a recipe can be recommended with a replacement ingredient, where appropriate. 1
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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