Classification of Spectroscopically Encoded Resins by Raman Mapping and Infrared Hyperspectral Imaging
Bibliographic record
Abstract
Barcoded resins (BCRs) were recently introduced as a potential platform for pre-encoded multiplexed synthesis, screening, and biomedical diagnostics. A key step toward the development of this strategy is the ability to rapidly interrogate and classify the BCRs in a high-throughput, noninvasive manner. Here, we describe a one-step strategy based on Raman mapping and Fourier transform infrared imaging to classify and spatially resolve randomly distributed BCRs. To illustrate this methodology, mixtures of up to 25 different BCRs were imaged and classified with 100% confidence. This strategy can be readily extended to a larger pool of resins, provided each BCR features a unique vibrational fingerprint (spectroscopic barcode). We have also established that reliable single-bead Raman spectra can be recorded in 10 ms, thus confirming that Raman mapping, in particular, could be a very fast method to classify the BCRs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".