Manufacture of Profiled Amabilis Fir Deckboards with Reduced Susceptibility to Surface Checking
Bibliographic record
Abstract
Machining grooves and ridges (peaks) into the surface of wooden deckboards reduces undesirable checking that develops when “profiled” boards are exposed to the weather. We aim to develop improved profiles for amabilis fir decking to reduce its susceptibility to checking, and also to examine whether profiling influences distortion (cupping) of deckboards. We systematically varied radii of grooves and peaks, and the heights and widths of peaks in profiled deckboards, exposed them to the weather, and measured checking and cupping of boards. Profiling significantly reduced the numbers and sizes of checks in amabilis fir deckboards, but increased cupping. Profiles with narrow grooves and tall peaks were generally better at restricting checking than profiles with wide grooves and shorter peaks. Our results suggest that one of the profiles we tested would be better at restricting checking than profiles used previously to manufacture profiled decking from amabilis fir. We conclude that the surface checking of profiled amabilis fir decking can be significantly reduced by altering the geometry of surface profiles. In principal, the same approach could be used with other softwood species that have potential to capture a share of the large and important market for wood decking.
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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".