Review of the effects of incising on treatability and strength of wood
Bibliographic record
Abstract
In wood, longitudinal fluid flow is several orders of magnitude greater than in radial or tangential directions. Incising of difficult-to-treat (i.e. refractory) wood species is a critical step in achieving adequate preservative penetration. Incising, as broadly defined, involves creating holes, incisions or fluid pathways to varying depths into the timber to increase longitudinal fluid flow and penetration into the wood. Incising has been used globally with early development occurring in the U.S., Canada, U.K. and Germany. It has been most heavily adopted in North America where it is required for treatment of thin sapwood lumber species in both the Canadian and U.S. treatment and engineering design standards. Incising can be either physical or biological. Physical incising uses teeth, knives, drills, needles, lasers, or high-pressure water jets to create pathways in the wood to the depth of the desired preservative treatment in a pattern that ensures uniform treatment. Biological incising uses bacteria or fungi to increase permeability. This review outlines the development, processes, applications and effects of incising technology. It specifically discusses their effects on treatability and strength properties, and reviews recent developments for modeling incising-related strength effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".