Prediction of long-term leaching potential of preservative-treated wood by diffusion modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An approach to modeling leaching and leaching impacts of preservative components from treated wood is presented based on three simple laboratory determinations: the amount of preservative component available for leaching (Le), equilibrium dissociation of preservative into free water in wood (Di) and diffusion coefficients for component leaching in different wood directions ( D ). In this study, the following inorganic wood preservative systems were investigated: chromated copper arsenate (CCA), the copper component of copper azole (CA) and alkaline copper quaternary (ACQ), and boron in disodium octaborate tetrahydrate (DOT). Aggressive leaching of finely ground wood showed that amounts of preservative compounds available for leaching were highest for borates, followed by copper in copper amine systems and arsenic in CCA, copper in CCA and chromium in CCA. The equilibrium dissociation or solubility of components in free water in the wood was much higher for borates and copper amine, followed by copper and arsenic in CCA and chromium in CCA. Use of the applicable diffusion coefficient ( D ) and Di or Le values in a diffusion model allows the prediction of total amount leached and emission or flux rate at different times of exposure for products with different dimensions and geometries. The approach was tested and generally validated through application of the model to results of laboratory water spray leaching of full-size lumber samples. The approach explains the rapid leaching of boron compounds (large diffusion coefficient and high initial dissociated concentration) compared to other preservative components and predicts that ACQ will have higher initial leaching rates compared to CCA and CA, but the latter preservatives will continue to leach copper at a measurable rate for a much longer time. The practical implications and limitations of the approach are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it