Effect of climatic variables on chromated copper arsenate (CCA) leaching during above-ground exposure
Bibliographic record
Abstract
Abstract Until recently, most research on the leaching of preservatives from treated wood was conducted in the laboratory. Although these studies have contributed to the understanding of leaching, the data generated under controlled conditions often do not apply to leaching during natural exposure and weathering. In addition, little is known about the effects of climatic variables and long-term emission rates. This paper examines leaching of chromated copper arsenate (CCA) from lumber exposed above ground to 685 mm of precipitationduring 351 days of natural weathering. Stepwise multiple regression analysis revealed relationships between leaching and a number of treatment and climatic variables, including: species of wood, initial preservative loading, amount of precipitation, average air temperature, average light intensity, pH and duration of rain event. Regression models explained approximately 30% of the variation in chromium and arsenic leaching and 44% of the variation in copper leaching.
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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".