Evaluation of the Impacts of Four Weathering Methods on Two Acrylic Paints: Showcasing Distinctions and Particularities
Bibliographic record
Abstract
Two commercial waterborne wood acrylic paints were applied to wood samples and the weathering resistance of samples was tested using four different weathering methods: outdoor exposure in Arizona (USA), Florida (USA), and the province of Quebec (Canada), and accelerated weathering in a QUV (fluorescent) weatherometer. Degradation was characterised by colorimetric and FTIR analyses. FTIR confirmed the importance of paint composition in the resistance of samples to weathering. Polymer sensitivity to UV radiation was clearly evident. An interpretation of discoloration in terms of either the energy received by the samples or the length of exposure is presented. Strong differences existed between the four weathering methods. Particularities of each method are discussed and recommendations regarding their application for effective testing are proposed. Overall, in addition to accelerated weathering tests, we conclude that it is necessary to test paints in an end-use environment for accurate assessment of their likely performance. This study confirms the multifactorial aspect of the weathering process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".