Bibliographic record
Abstract
In the current scientific literature, one can find >100 different methods to evaluate water-holding capacity in fresh and cooked meat. The main concepts are based on removing some of the water by either gravity, application of pressure (e.g., centrifugal force), and heating while measuring water exudate to predict the water holding capacity (WHC) during storage, processing, cooking, and/or distribution. More sophisticated methods include nuclear magnetic resonance (NMR) in which the relaxation of water molecules within a meat protein/gel system is measured to predict how the water (75% in lean meat) will behave during processing. Overall, the number of tests reported is also so high because there are quite big variations in test conditions (e.g., 750-30,000 g for centrifugal testing). The aim of this article (outcome of a symposium on methods for poultry meat characterization) is to help the reader navigate through the different setups and suggest standardized testing based on scientific principles. The recommended WHC test is the application of low centrifugal force (750 g so sample is not permanently deformed) to a protein gel, while the sample is placed on a screen platform to avoid reabsorbing the liquid separating during the slowing down of the centrifuge. It is also recognized that some meat samples (e.g., high in fat) might require a different g-force, so it is recommended to employ both the conditions mentioned above and the lab-specific conditions. Our overall goal should always be to increase uniformity in test procedures, which will enhance our capabilities to compare results among research groups.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".