Bibliographic record
Abstract
I love food. I love cooking, baking, testing, and eating. I read about food preparation, food facts, and food service. Over the years I’ve developed my fair share of knowledge about cooking and I’m a decent cook, but I’m no chef. I guess I’m what you’d call a “foodie”. However, I have the good fortune to have a friend who is a chef and owns one of the best, and certainly the most innovative, restaurants in town. During this summer I hosted a cooking class in my home for my family with my chef friend as instructor. The Tex-Mex barbecue theme was a big hit (you can contact me for recipes, if you like), but much more fascinating was the explanation of the science behind the cooking. It turns out that there is a term for this: molecular gastronomy. Another term, and hence the genesis of my “Eureka!” moment of the summer, is evidence based cooking. Good cooking is not just following a recipe (not all of which are evidence based) but at its best is the culmination of heaps of tested information regarding why and how chemical and environmental factors work together to result in a gastronomical delight. For example, will brining or marinating a pork chop make it moister? And, if brining, what temperature should the water be, how long should it soak, and how much salt is needed? Why does pounding meat increase its tenderness? What will keep guacamole from browning better – the pit or lime juice? What does baking soda do in a chocolate cake? Eggs or no eggs in fresh pasta?
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 | 0.115 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.173 | 0.059 |
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".