Radial distribution of vascular bundle morphology in Chinese bamboos: machine learning methodology for rapid sampling and classification
Bibliographic record
Abstract
Abstract Variation in anatomical features of the culm wall namely the shape and size distributions of vascular bundles between different genera and species of bamboo is not well understood due to the cumbersome task of manual measurements. Using machine learning methodology, this work presents a universal vascular bundle detection model for rapid, reliable, and automatic characterization of vascular bundles in culm cross sections of 213 species across 23 genera of Chinese bamboos. The number of vascular bundles and the fiber sheath area have positive linear correlations with the outer circumference and the wall thickness, respectively. The distribution density of vascular bundles has a decay exponential correlation with the outer circumference and the wall thickness. The average fiber volume fraction was 35.2 % ± 7 % with relatively small variation between species. Bamboo species could be grouped into three categories based the endodermis to epidermis distribution pattern of radial and tangential length of vascular bundles, two categories of radial-to-tangential ratio and four categories of fiber sheath area distribution pattern. Implications on bamboo classification, structural and pulp/paper applications were discussed. The findings from this study provide groundwork for the establishment of a unified, authoritative and objective bamboo classification system based on the vascular tissue morphology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".