Replication Data for: Raman Spectra for Plastics Identification (RaSPI) and Raman Maps for Plastics Identification (RaMPI) Research
Bibliographic record
Abstract
Plastics pollution is a pervasive global issue and machine learning (ML) is gaining traction as a means to facilitate plastics monitoring. The significant interest in Raman spectroscopy in this field is impacted by high variability in publicly available Raman spectra and classification labels. To address this, we provide a collection of 402 high-resolution Raman spectra (Raman spectra for plastics identification; RaSPI) and 34 two-dimensional Raman spectroscopic maps containing 32,389 additional spectra (Raman maps for plastics identification; RaMPI). The spectra offer samples from diverse sources, including from environmental pollution, variability in (unknown) additives, high signal:noise ratio, and continuous data with <1 cm-1 spacing between 100 and 4000 cm-1 (RaSPI) or 700 and 1800 cm-1 (RaMPI). All spectra have been manually assigned as one of 14 different plastic type classifications. The consistency and quality of these datasets make them high-value resources for researchers active in a range of topics, including training ML models for microplastics research, for developing spectroscopic processing algorithms, or for those seeking datasets to test their methodologies against.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.061 |
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".