Three-dimensional visualization of the vascular bundle in a branched bamboo node
Bibliographic record
Abstract
Bamboo is a natural vascular bundle (VB) reinforced composite material used in more than 10 fields such as construction and furniture. The nodes in bamboo are crucial to its mechanical properties, but understanding of its performance is limited by lack of knowledge about the three-dimensional (3D) structure of the node. This work aimed to non-destructively identify the multi-dimensional characteristics of the VB in a bamboo branched node (BN) using X-ray microtomography (µCT). The VB was segmented from the BN using deep learning combined with the Watershed algorithm. The 3D model reconstruction and characterization of the VB were also conducted. It was found that the structure of VBs showed significant changes along the height of the BN. The VBs formed a complex 3D structure, VBs of the culm are connected with those of the branch, and the connectivity of the conducting tissue and fibers was 88.91% and 99.95%, respectively. The conducting tissue and the fibers had similar shapes but varying thicknesses, which enabled VBs to perform both water transport and mechanical support functions. The volumes fraction of parenchyma, fibers, and conducting tissue in the BN were 61.3%, 35.3%, and 3.4%, respectively, but the tissue proportion of the different heights of the BN varied from each other. The nodal ridge was a mechanical weak point of the BN, with a maximum fibers proportion of 43.8%. This study contributes to understanding the relationship of VBs between the branch and the culm. It provides a structural perspective for understanding the mechanical properties of BN and a theoretical basis for optimizing bamboo utilization efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".