EVALUATION OF NUMERICAL ALGORITHMS FOR THE INSTRUMENTAL MEASUREMENT OF BOWL‐LIFE AND CHANGES IN TEXTURE OVER TIME FOR READY‐TO‐EAT BREAKFAST CEREALS
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 Cornflakes were immersed in milk, rapidly drained and compressed in a TA. XT2i texture analyser (Stable Micro Systems, UK) fitted with an Ottawa Cell. The data were analyzed numerically yielding nine instrumental crispness parameters. Bowl‐life was determined using an untrained sensory panel. Three models (Weibull, exponential and modified exponential) successfully modeled the change in mechanical properties as a function of immersion time. An instrumental method of measuring bowl‐life is described that measures peak force at a range of immersion times and models the data with the Weibull equation. This method may be a valuable asset to the breakfast cereals industry.
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 | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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