Bibliographic record
Abstract
We describe a spectral-focusing-based polarization-resolved coherent anti-Stokes Raman scattering (SFP-CARS) microscopy system developed by making simple and inexpensive modifications to an existing spectral focusing CARS setup. By using the system to study polarization dependent features in the CARS spectrum of benzonitrile, we assess its capabilities and demonstrate its ability to accurately determine Raman depolarization ratios. Ultimately, the detected anti-Stokes signals are more elliptically polarized than expected, hindering a complete suppression of the non-resonant background. Furthermore, the fact that resonant signals polarized in directions similar to that of the non-resonant background are also substantially suppressed when extinguishing the non-resonant background remains a serious limitation. We conclude that non-resonant background suppression using the SFP-CARS system is best suited for studying Raman modes that generate signals polarized in directions far from that of the non-resonant background instead of for obtaining background-free CARS spectra. In all, we find that the SFP-CARS setup is a useful tool for studying polarization dependent features in the CARS spectra of various samples that is worthy of further investigation. This work aims to illuminate several technical aspects of polarization dependent CARS and inform researchers of the benefits and constraints of integrating polarization dependent detection as an add-on to existing CARS microscopy setups.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".