Comparison of the Adhesive Performances of Soy Meal, Water Washed Meal Fractions, and Protein Isolates
Bibliographic record
Abstract
Adhesive bonding of wood plays an increasing role in the forest products industry and is a key factor for efficiently utilizing timber and other lignocellulosic resources. In this work, we obtained five soy meal products through commercial sources or in-house preparations. The protein content was 49.6%, 56.9%, 66.2%, 86.3%, and 91.9% for untreated defatted soy meal, pH 8.5 water washed meal, neutral water washed meal, commercial protein isolate, and in-house prepared protein isolate. The adhesive performances measured by the maximal dry and soaked shear strength of the bonded maple veneers at break were not exactly in the same order of the protein content, indicating that other components (e.g. carbohydrates, metals) might also have played certain roles in the adhesive ability of these products. Data at two press temperatures (i. e. 100, and 130 oC) with or without the addition of tung oil revealed that water washed soy meals behaved more like untreated meal than soy protein isolates. This observation is different from a recent report on the effect of water washing on cottonseed meal products. Thus, further elucidation of the mechanisms or causes of the differing effects of water washing would shed light on the adhesive mechanisms of the two types of oilseed meal materials, thus optimizing use of these materials and their fractions for wood bonding.
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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".