Comparision of Different Raw Densities and Board Thicknesses in the Production of Enzymatically Bonded Fiberboards
Bibliographic record
Abstract
Reducing CO2 emissions for the long term is an important issue in countering climate change. For this reason, the use of renewable construction materials inter alia wood-based materials is becoming increasingly important. Wood based materials often depend on petrochemical-based binding agents, most of which contain and emit formaldehyde. This causes environmental and health-related difficulties that could be rectified by avoiding the utilization of these adhesives. For this purpose, enzymatically bonded medium-density fiberboards (MDF) with a targeted raw density of 650 kg/m³ and 750 kg/m³ and high-density fiberboards (HDF) with a density of 900 kg/m³ were produced in different thicknesses. Laccase-Mediator-System (LMS) was applied to manufacture the boards and to compare them to those glued with UF and inactivated laccase, respectively. These panels were tested in terms of their physical technical properties, such as internal bond strength (IB), modulus of rupture (MOR) and thickness swelling (TS) after 24 h immersion. The aim was to show the correlation between density and these properties for fiberboards bonded this way. Most panels meet the requirements of the European standard when LMS and UF-resin were used. The possibility to produce fiberboards bonded with alternative bonding agents opens up the way to alternative, innovative, healthy and environmental-friendly wood based products.
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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 | 0.001 |
| 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".