RamanCrystalHunter: A new program and database for processing, analysis, and identification of Raman spectra
Bibliographic record
Abstract
Abstract RamanCrystalHunter (RCH) is a new software program designed to pre-process, analyze, and identify Raman spectra by comparison with spectra in the RamanCrystalHunter Database (RCHDB). The software is free and can be downloaded from the website https://www.fabrizionestola.com/rch. RCH is characterized by a simple graphical user interface, making it suitable for both specialist and non-specialist users, and it has been developed mainly for applications in Earth Sciences (processing the spectra of minerals) but can be used to process the Raman spectra of any synthetic or natural inorganic or organic material. RCH allows users to visualize, pre-process (e.g., using smoothing, noise reduction, and baseline correction operations), and analyze (e.g., using fitting or various calculation tools) Raman spectra. Moreover, it is equipped with the RCHDB, a new database of high-quality mineral spectra that can be downloaded for free, along with the RCH program. The RCHDB contains the Raman spectra of minerals (including single- and multi-phase inclusions within mineral hosts, for example, diamonds) and related synthetic compounds, allowing for rapid and accurate identification of unknown spectra. The RCH software includes highly customizable yet efficient and user-friendly methods for processing and analysis of Raman spectra and represents a valuable contribution to the field of Raman spectroscopy, whose applications have expanded greatly in recent years, especially in Earth Sciences. Two practical examples of novel ways in which this software can be used for geoscience applications are presented.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.029 |
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".