Bibliographic record
Abstract
Many of you will know that NZIFST is a member of the International Union of Food Science and Technology (IUFoST), based in Canada, an organisation of national professional food science societies. Every two years a World Congress is held by IUFoST, the latest being the 17th in Montreal, Canada, August 17-21 this year, with around 2000 attendees. The opportunity arose to present some of my research during one of the scientific sessions at the Congress and represent NZIFST at the General Assembly along with Past-President, Laurie Melton, FNZIFST. IUFoST has an international outlook that focuses on addressing some of the world's food problems such as food security, nutritional quality, distribution, and very importantly, education, so that people in developing countries can establish sustainable agriculture and food industries. This particular Congress was hosted by our sister organisation, the Canadian Institute of Food Science and Technology. The six-day conference was certainly a jam-packed event.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.132 | 0.040 |
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".