Fractal dimension of wood pores from pore size distribution
Bibliographic record
Abstract
Abstract Understanding wood pore geometry and distribution is the basis for studying its macroscopic properties. This research investigated the pore fractal dimension using the pore size distribution of selected softwoods and hardwoods. Mercury intrusion porosimetry explored the detailed structural parameters of wood pore size distributions and assessed their fractal dimension. The results revealed significant variability in pore size distribution, porosity, pore volume, and fractal dimension values. The threshold pressure extracted from porosimetry data can be used as the main parameter to distinguish the pore size distribution regions. Pore sizes ranged from 3 to 35,000 nm, with a corresponding porosity that ranged from 58 to 76%. Three pore size classes were determined and ranged as: macropores (radius 350,000–5000 nm), mesopores (radius 5000–100 nm), and micropores (radius 100–3 nm). The fractal dimension values in the corresponding macropore, mesopore, and micropore size intervals were 2.98–2.998, 2.6–2.92, and 2.53–2.72, respectively, indicating a higher degree of complexity for larger pores.
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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 | 0.000 |
| 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".