Protocol Comparison: Laboratory versus Natural Weathering Tests for Performance Evaluation of Coatings on Preservative-Treated Wood
Bibliographic record
Abstract
Abstract Fourteen stains were tested in the laboratory to compare water uptake and leaching reduction of wood treated with chromated copper arsenate, alkaline copper quat, and copper azole. Based on results of a 2-week test, eight stains were selected to be evaluated over 3 months of accelerated weathering and five stains over 3 years of natural exposure in Toronto, Ontario, Canada. These comparisons were made in order to find a quick and reliable method for replacing natural exposure tests. Comparison of different weathering techniques showed significant correlations between leaching and water uptake results from laboratory tests and natural weathering. The cumulative percentage of inorganic elements leached from coated samples was highly correlated with the cumulative percentage leached during 3 years of natural weathering. Also, the average moisture content of treated-coated samples after 1 and 3 days of water immersion showed a relatively strong positive correlation with the average moisture content of treated-coated wood samples during wet periods of natural weathering (moisture content above 20%, average of 17 reading times). Thus, this quick laboratory test is a reliable short-term test for evaluating the ability of coatings to reduce leaching and water uptake when applied on preservative-treated wood.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".