Bibliographic record
Abstract
Abstract The use of microscopy and imaging methods to study foods is not a new idea. It has been going on since the first light microscopes were developed (White, 1970; White and Shenton, 1974-1984). Microscopy has been used to determine the quality, purity and safety of foodstuffs by detecting and identifying contaminants in foods. The short article by Stephen Carmichael in the May/June 2002 issue of Microscopy Today has again brought food microscopy into the spotlight. The article provided an opportunity to discuss present applications of food microscopy and to give some projections of where it is headed in the future. The reader may not realize that microscopy and imaging methods are used extensively by most major food companies worldwide for product development, quality control, and trouble shooting (Allan-Wojtas, 1999). Often, this work cannot be published because it contains proprietary information. The application of microscopy to food structure analysis is one of the best kept secrets in microscopy today.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".