Effect of conditioning history on the characterization of hardness of thermo-mechanical densified and heat treated poplar wood
Bibliographic record
Abstract
Abstract Poplar wood was modified by a combination of thermo-mechanical densification (TMD) and heat treatment (HT) process at five temperatures ranging from 170 to 210°C. A new two-step conditioning method (CM) is suggested, in the course of which the modified wood is submitted to 50°C/99% RH→25°C/65% RH, where RH means relative humidity in the climate chamber. The traditional one-step CM (25°C/65% RH) served as reference. The effects of conditioning history on hardness were observed and analyzed along with the change of dimensional stability. The hardness of the modified wood was lower in the case of the proposed CM due to more set-recovery release, but the extent of that decreased with the HT temperature. For a good hardness, HT 200°C should be selected with the proposed CM, which is different from the optimization output of 180°C obtained from the traditional CM. In conclusion, a specific assessment method for the performance characterization of this type of modified wood would be beneficial for the combined TMD and HT processes.
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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 | 0.000 | 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 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".