Femtosecond Laser Filamentation for Atmospheric Sensing
Bibliographic record
Abstract
Powerful femtosecond laser pulses propagating in transparent materials result in the formation of self-guided structures called filaments. Such filamentation in air can be controlled to occur at a distance as far as a few kilometers, making it ideally suited for remote sensing of pollutants in the atmosphere. On the one hand, the high intensity inside the filaments can induce the fragmentation of all matters in the path of filaments, resulting in the emission of characteristic fluorescence spectra (fingerprints) from the excited fragments, which can be used for the identification of various substances including chemical and biological species. On the other hand, along with the femtosecond laser filamentation, white-light supercontinuum emission in the infrared to UV range is generated, which can be used as an ideal light source for absorption Lidar. In this paper, we present an overview of recent progress concerning remote sensing of the atmosphere using femtosecond laser filamentation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".