Ultrashort-pulse burst-mode materials processing and laser surgery
Bibliographic record
Abstract
Laser processing of materials and biological tissues has evolved in stages, ever since the earliest use of the laser for gross deposition of heat and for ablation. For instance, wavelength specificity was an early development that facilitated the treatment of certain biological tissues, while leaving others relatively unaffected. Ultrashort-pulse material ablation escapes the usual paradigm of heat diffusion because of the comparisons of scales: A rarefaction wave can cut through the thin layer of femtosecond-laser-heated material and carry away the absorbed energy before much heat can diffuse into the substrate. Burst-mode femtosecond laser ablation brings yet another paradigm, in which the laser fluence is divided over two disparate timescales: the ultrashort duration of a pulse and the microsecond-scale duration of a burst. This division of timescales opens new avenues for control, because much of the governing physics is about the comparison of timescales—for instance, the timescale of thermalization of heated electrons into the substrate lattice or the timescale of hydrodynamic ablation. Applications to fused silica, to in vitro cell-cultures prepared in hydrogels, and to ex vivo articular cartilage help to show what is different in the science of ultrashort-pulse burst-mode laser processing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".