Improving the Performance of Clear Coatings on Wood through the Aggregation of Marginal Gains
Bibliographic record
Abstract
Remarkable increases in the performance of complex systems can be achieved by a collective approach to optimizing individual factors that influence performance. This approach, termed the aggregation of marginal gains, is tested here as a means of improving the performance of exterior clear-coatings. We focused on five factors that influence clear-coating performance: dimensional stability of wood; photostability of the wood surface; moisture ingress via end-grain; coating flexibility and photostability; and finally coating thickness. We performed preliminary research to select effective wood pre-treatments and durable clear-coatings, and then tested coating systems with good solutions to each of the aforementioned issues (factors). Red oak and radiata pine panels were modified with PF-resin, end-sealed, and thick acrylic, alkyd or spar varnishes were applied to the panels. Panels were exposed to the weather and the level of coating defects was assessed every year over a 4-year period. All of the coatings are performing well on PF-modified pine after 4 years’ outdoor exposure. In contrast, coatings failed after 2 years on unmodified pine and they are failing on PF-modified oak. We conclude that our approach shows promise. Future research will build on the current work by developing solutions to additional factors that influence clear-coating performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".