Effect of preservative type and natural weathering on preservative gradients in southern pine lumber.
Bibliographic record
Abstract
The effects of preservative type and natural weathering on preservative component distribution in southern pine boards were evaluated. Lumber was treated by a modified full-cell process with chromated copper arsenate (CCA-C), alkaline copper quat (ACQ-D), and micronized copper quat (MCQ), and samples were exposed to natural weathering. After treatment, the copper and arsenic components of CCA were uniformly distributed across the board thickness, whereas the chromium component was higher near the surface. The copper amine component of ACQ was preferentially adsorbed near the board surface, whereas MCQ had lower copper concentration near the surface compared with inside the board. The quat component (didecyldimethylammonium carbonate [DDACb]) of both preservatives was preferentially adsorbed near the surface resulting in a steep concentration gradient. After 330 da of exposure to natural weathering, the average amounts leached were 2.9% for ACQ-Cu, 0.36% for MCQ-Cu, 0.24% for CCA-Cr, 0.59% for CCA-Cu, and 2.05% for CCA-As. For ACQ and MCQ, the ratio of CuO to quat increased significantly with weather exposure indicating a higher DDACb rate of leaching compared with copper. For both preservatives, it was estimated that DDACb leaching was about 20% for ACQ and 16% for MCQ.
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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 | 0.000 |
| 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) | 0.001 | 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".