Measurement of Spaghetti Speck Count, Size, and Color Using an Automated Imaging System
Bibliographic record
Abstract
ABSTRACT An objective imaging method was developed to count dark specks in spaghetti. The method simultaneously measured individual speck size and color and the overall color of the spaghetti. Spaghetti samples were prepared from durum wheat samples collected from the Prairie Registration Recommending Committee for Grains (PRRCG) durum wheat cooperative trials during four consecutive crop years from 2002 inclusive to 2005. Differences in speck counts were found between samples within each year. From year to year, the baseline for speck counts varied with the highest numbers in 2005 and the lowest numbers in 2004. For comparison, three technicians also counted the number of specks in each sample. These visual counts were not consistent between technicians or technician to the imaging method, supporting the need for this objective approach. Spaghetti speck counts did not relate to the speck counts of the semolina subsequently used to prepare the product. Speck sizes were consistent across samples and between years, indicating a consistent milling method for all the samples. Differences in speck count numbers could not be attributed to differences in speck color or pasta color. The imaging method gave very consistent speck counts and color measurements over the four years.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".