Accuracy of estimating the technological and economic value of pig carcass primal cuts with an optical-needle device
Bibliographic record
Abstract
The aim of this study was to demonstrate the possibility of using CGM-device measurements for the precise estimation of lean-meat content, the real technological and economic value of ham and loin. The experiment was carried out in two stages: the dissection (n = 136) and the industrial cutting (n = 298). Lean-meat content in carcasses was defined with a CGM device, where the thicknesses of the musculus longissimus dorsi (M2) and backfat (with the skin) (T2) were measured. The strongest correlations were achieved for the T2 measurement and the share of skin with fat or muscles in ham and loin. The strongest correlations for M2 were noted for muscle content in ham and loin. Measurements for M2 positively correlated with all the elements of the technological cutting of ham and loin, but negatively with T2 measurements, in terms of commercial value. The highest values, regardless of the point of measurement, were noted for boneless loin (loin) and ham 4D (ham). The highest estimating accuracy was noted for dissection and technological cutting of loin compared with ham. The results of the study suggest that the current classification device should be modified and improved for the evaluation of the real commercial value of the carcass.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".