Structural Modelling of Fish as Applied to Portion Control in Automated Canning
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
Accurate and high-speed generation of a product portion for packaging or canning is of great industrial significance. Optimal portion control refers to making the portion as close as possible to a desired net weight, according to some cost function. A technique of optimal portion control has been developed for can-filling of fish. This approach requires fast on-line measurement of the weight distribution function of each incoming fish in the process line. Direct measurement of this function, which is known to be complex, is not feasible by conventional means, particularly in view of the cost considerations and the necessary production speeds. An indirect approach has been developed that is suitable for on-line measurement of the weight distribution of fish. The approach depends on off-line experimentation and model development, and use of these models on-line in conjunction with fast on-line measurements of simple geometric attributes of each incoming fish. A comprehensive structural model for fish body is developed in the present article, based on real data and practical information on fish body shapes. The model is used to provide reasonable and extensive data on fish, in a quick and cost-effective manner. This approach of model-based data generation is particularly useful when large-scale gathering of mechanical data on fish is not feasible. Procedures and steps of model development are presented in the article. Accuracy of the model is evaluated using real data on a batch of salmon. The related issue of the performance of the on-line sensor for measuring weight distribution function of fish is addressed.
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 | 0.000 | 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