Length Distribution and Other Dimensional Parameters of Chopped Forage by Image Analysis
Bibliographic record
Abstract
Abstract. Traditional particle size analysis of chopped forage is done by mechanical sieving, thereby providing mass distribution of one dimension. Recent studies have shown that long and narrow particles can tip during shaking and slide through holes smaller than the longest particle dimension. Meanwhile, well calibrated image analysis is definitely more accurate than screening in measuring true dimensions of chopped particles. An experiment was carried out with chopped alfalfa and corn harvested at three theoretical lengths of cut (TLOC = 4.8, 9.5, and 11.1 mm). Particles were initially sorted by the ASABE standard screening method. Particles within each screen were spread on a flat surface and photographed. Pictures were processed with the Image Analysis Toolbox in MATLAB, providing total pixel area, vector length (greatest distance between two points on the periphery), and an estimate of width for individual particles. All particles per picture were weighed, providing an estimate of volume and the third dimension (thickness). The ASABE standard method underestimated particle length as measured by image analysis by an average of 31%. Width was not significantly different for alfalfa particles at three TLOC, as expected, but it increased for corn as TLOC increased, indicating breakage in two dimensions (length and width) due to the bulky nature of corn. Image analysis and mass measurements provided detailed information on total outer surface area per unit mass, with an average of 218 cm2 g-1 dry matter (DM) for alfalfa particles and 133 cm2 g-1 DM for corn particles. Combining image analysis and mechanical sieving improved the estimation of mass and dimensional parameters.
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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 | 0.003 | 0.002 |
| 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) | 0.009 | 0.003 |
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".