Review of the effects of incising on treatability and strength of wood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it