Comparison of the content of lean meat in pigs on farm and slaughter line
Bibliographic record
Abstract
Measurement of lean meat on slaughter line and formation of price on the basis significantly contribute to the overall improvement of the quality and profitability of production and distribution of pork. The content of lean meat on live pigs was measured on farm using ultrasound device PIGLOG 105. While in slaughterhouse, the content of lean meat measured using Fat-O-Meater (FOM), two-point method (TP) and partial dissection. 59.30% of lean meat in vivo was estimated by the apparatus PIGLOG-105 one day before slaughter. It is 0.91% more then partial dissection and when compared to FOM and TP it is more 4.86% and 4.02%. Great deviation between PIGLOG-105 on one side and FOM and TP on other side indicated some error, and then partial dissection solved this mystery. After this study, slaughterhouse constructed new formulas for FOM in pig carcass classification. Regarding that, slaughterhouses which used FOM or similar equipment for measuring percentage of lean meat, should control results of the equipment described in this study, minimum twice a year.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".