Size and Shape of Forage Particles by Image Analysis and Normalized Multiscale Bending Energy Method
Bibliographic record
Abstract
Abstract. Traditional particle shape descriptors (e.g. sphericity, elongation, convexity, etc.) have shown a lack of robustness because of their inability to provide a unique solution. The Normalized Multiscale Bending Energy method (NMBE) provides a unique shape signature from individual particle contour morphology. The method is based on the convolution’s theorem for the low-pass Gaussian filtering of parametric curves. It determines quantitatively the shape features at different scales, notably at small scale (irregularity) and large scale (elongation). The method is built around MATLAB® software using Image Toolbox Processing to treat 2D static pictures of particles spread on a flat area. Chopped alfalfa and corn particles (12.7, 25.4 and 29.6 mm of theoretical length of cut – TLOC) were investigated in this study. Image analysis enables to calculate geometric features of forage particles especially length, area and perimeter. An individual particle mass approximation allows building curves of cumulative mass as a function of length for a given sample. Mechanical sieving preprocessing enables to establish links between 1D particle size distribution, image analysis and shape distribution. Furthermore, it has been observed that measurement by mechanical sieving systematically underestimate the particle length (by 28.8% for corn and 72.4% for alfalfa at median length). The method successfully characterizes particles morphology in terms of elongation and irregularity.
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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 | 0.000 | 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 | 0.000 |
| 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".