Bright Phase-Stable Waveforms Covering the Entire Infrared Molecular Fingerprint Region
Bibliographic record
Abstract
The recent availability of modelocked-laser architectures emitting MHz-repetition-rate trains of near-infrared (NIR) pulses with multi-W average powers and 10-fs-scale durations has opened up new vistas for spectroscopy in the mid-infrared (MIR) spectral range. Parametric downconversion processes driven by these femtosecond lasers in suitable nonlinear media, in particular intrapulse difference-frequency generation (IPDFG), afford combinations of the desirable properties of MIR sources: broad spectral coverage, high brilliance, and spatial and temporal coherence. Yet, unifying these in a robust and compact radiation source has remained a challenge. Here, we address this challenge by means of IPDFG in a multi-crystal in-line geometry, driven by the powerful, 10.6-fs pulses of a 10.6-MHz-repetition-rate, nonlinearly post-compressed Yb: YAG thin-disk oscillator [1], [2].
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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) | 0.004 | 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".