An Explanation of Subsurface Optical Pathways through Food Myosystems and their Effect on Colorimetry
Bibliographic record
Abstract
Light may pass along and across the long axes of muscle fibers in any food myosystem. Thus, incident light may be scattered in several ways before some of it reappears at the surface as diffuse reflectance. Pathways may be short if scattering is strong, or long if scattering is weak. Short pathways minimize selective absorbance by chromophores such as myoglobin, while long pathways maximize selective absorbance. Many food myosystems exhibit a post-mortem decrease in pH caused by anaerobic glycolysis with a series of microstructural changes – glycogen granules between myofibrils are lost, myofibrils shrink laterally as myofilaments move closer together, water moves from within myofibrils to the space between them, muscle fiber membranes leak and lose their electrical capacitance, and myoglobin is flushed into the fluid filled spaces between muscle fibers. These changes increase scattering of light passing across the long axes of muscle fibers. Scattering of light along muscle fibers is caused by sarcomere discs (A-bands). Interference from one or a small number of sarcomere discs may cause iridescence, but in most cases interference from numerous discs causes achromatic diffuse reflectance. Commission International de l’Éclairage (CIE) chromaticity coordinates were calculated for various subsurface optical pathways. Pathways across versus along muscle fibers had a strong effect on CIE y (r = 0.84, P < 0.01) and an even stronger effect on CIE Y% (r = 0.95, P < 0.005).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".