Effect of steam-heat treatment on mechanical properties of Chinese fir
Bibliographic record
Abstract
Heat treatment often brings about some negative effects on mechanical properties of wood. Chinese fir is currently underutilized due to some inherent properties that limit its further applications. Using steam as a heating medium and a shielding gas, the heartwood and sapwood of Chinese fir were treated at a temperature ranging from 170ºC to 230ºC and time from 1 to 5 hours in an airtight chamber. Both the modulus of rupture (MOR) and modulus of elasticity (MOE) were increased for the sapwood specimens under the temperature less than 200ºC for short treatment times. The hardness was increased for both two kinds of specimens under the temperature less than or about 200ºC, compared to the untreated specimens. The temperature has a stronger effect on mechanical properties of wood than the time, and the temperature of 200 ºC is a critical point in modifying mechanical properties of wood.
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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) | 0.002 | 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".