Reuse of the FoodOn ontology in a knowledge base of food composition data
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
We describe our work to integrate the FoodOn ontology with our knowledge base of food composition data, WikiFCD. WikiFCD is knowledge base of structured data related to food composition and food items. With a goal to reuse FoodOn identifiers for food items, we imported a subset of the FoodOn ontology into the WikiFCD knowledge base. We aligned the import via a shared use of NCBI taxon identifiers for the taxon names of the plants from which the food items are derived. Reusing FoodOn benefits WikiFCD by allowing us to leverage the food item groupings that FoodOn contains. This integration also has potential future benefits for the FoodOn community due to the fact that WikiFCD provides food composition data at the food item level, and that WikiFCD is mapped to Wikidata and contains a SPARQL endpoint that supports federated queries. Federated queries across WikiFCD and Wikidata allow us to ask questions about food items that benefit from the cross-domain information of Wikidata, greatly increasing the breadth of possible data combinations.
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 | 0.001 | 0.001 |
| 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