Prediction of the color variation of moso bamboo during CO2 laser thermal modification
Bibliographic record
Abstract
Thermal modification is widely used for bamboo materials as an efficient modification method. CO2 laser with the advantages of high energy density, short process period, non-pollution, etc. could be applied as a novel thermal treatment for wooden and bamboo materials processing. The laser intensity argumentation of power, motion arguments of feed rate, and sweep width for laser emitter were selected as input arguments for treating the Moso bamboo surface. The lightness variation and total color variation (∆L* and ∆E*) were collected using a portable colorimeter to describe the bamboo surface color variation after laser irradiation. Response surface methodology was chosen for designing experiments and modeling. The results showed that the increase of laser power had a positive influence on increasing the absolute values of ∆L* and ∆E*, but the feed rate of laser emitter and sweep width increasing had opposite effects on absolute values of ∆L* and ∆E*. The quadratic models of ∆L* and ∆E* created by response surface methodology were competent for describing the relationship between laser processing arguments and color indexes of ∆L* and ∆E*. This approach will be useful for selecting suitable and desirable processing arguments to get the surface color of bamboo productions.
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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) | 0.000 | 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 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".