Optimizing Surface Micro Grooving to Reduce the Checking and Cupping of Douglas Fir, Western Hemlock and White Spruce Decking Exposed to Natural Weathering
Bibliographic record
Abstract
Machining grooves into the surface of pine and fir (Abies spp.) deckboards reduces undesirable checking that develops when “profiled” boards are exposed to the weather. We aim to develop improved profiles for Douglas fir, western hemlock and white spruce decking to reduce their susceptibility to checking, and understand how profile geometry influences the stresses that cause checking. We varied the width and depth of grooves in profiled deckboards, exposed deckboards to the weather, and measured checking and cupping of boards. A numerical model examined the effect of groove depth on the moisture-induced stresses in profiled spruce boards. Profiling significantly reduced checking, but increased cupping of deckboards made from all three species. Western hemlock checked more than the other two species. Profiles with narrow grooves (rib profiles) were better at restricting checking than profiles with wider grooves. A rib profile with deeper grooves developed smaller stresses than a rib profile with shallower grooves, and boards with the former profile checked less than boards with shallower grooves. We conclude that checking of profiled Douglas fir, western hemlock and white spruce decking is significantly reduced by changing profile geometry, and our results suggest the best profiles to reduce checking of all three species.
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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".