Modelling borate formulations through pressure-treated sectional three-layer cross-laminated timber structures by near-infrared spectroscopy
Bibliographic record
Abstract
The present study examines the borate distribution of water-borne preservative (Timbor and Boracol) formulations into sectional three-layer cross-laminated timber (CLT) structures that were pressure-treated using a full-cell process for exterior exposures to ensure long-term durability. Both commercial preservatives, dispensed through three-layer CLT structures, are formulated with the DOT (from disodium octaborate tetrahydrate) dissolved in a propylene and/or monoethylene glycol solvent, which facilitates the chemical ingredients’ impregnability and correlates with the potential of near-infrared spectroscopy (NIRS) for estimating. This was conducted throughout an orthogonal clear-cut of pressure-treated three-layer CLT structures of Eastern black spruce (Picea mariana var. Mariana) species at different experimental (0, 5, and 14 days) conditions. Cross-sectional and perpendicular cuts of CLT elements described three glued layers of lamination (known as lamella) arranged orthogonally to each other, which were used for measuring depths of borate penetration. As part of evaluating the borate's diffusion gradient in terms of boric acid equivalent (BAE), the edge of wood grain directions of half portions of the three-element CLT structures were sliced every 0.5 cm ( 3/16 -inch), following borate impregnation. The investigation was undertaken through CLT upper (face grain) and lower (back grain) members, as well as into CLT transverse (radial/tangential) grain directions and the centres (along the longitudinal core grain directions) of inner CLT structures. Validation models achieved statistical values of R 2 of 0.75 and 0.76 for Boracol and Timbor formulations based on DOT-treated ingredients, respectively. The root mean square error ranged from 0.5611 to 1.2813% BAE, and the average margin of standard deviation of DOT (Timbor and Boracol) formulations varied significantly from 0.6 to 1.49 (±0.0025) through the performance of sample-specific standard error of borate prediction analysis. The potential of NIRS shows some predictive abilities using the projection to latent structures or partial least squares regression method, including the sample-specific standard error of borate prediction analysis for estimating differences in lamellas of CLT three-layer untreated and full-cell pressure-treated with formulations based on DOT ingredients in cases of exterior exposures.
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.
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 | 0.001 | 0.001 |
| 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 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".