Post-2000 progress in nonlinear optics: tabulating new materials and best practices
Bibliographic record
Abstract
The field of Nonlinear Optics (NLO), launched about 60 years ago, has gained considerable momentum over the past two decades, resulting in an enormous growth in NLO publications for a wide range of material categories, including bulk materials, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, and hybrid waveguiding systems. However, a convenient summary of NLO data collected since 2000 for these different material types has been lacking and would be a valuable resource for researchers in the field. Here, we present a new set of data tables showcasing a representative list of NLO properties taken from the literature since 2000 on the above-mentioned material categories. Furthermore, we provide best practices for performing and reporting NLO experiments. These best practices underpin the selection process that we used for including papers in the tables, and also form the foundation for a more adequate comparison, interpretation, and use of the NLO parameters published today and those that will be published in the future.
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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 | 0.036 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.048 | 0.056 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".