Bibliographic record
Abstract
Abstract Molecular spectroscopy as provided using the near‐infrared (NIR) measurement technique is valuable for polymer identification, characterization, and quantitation. NIR spectroscopy can be completed for in situ process applications where no sample preparation is required, and where rugged optical systems are a necessity. The NIR region is a complimentary band of the electromagnetic spectrum to the mid‐infrared (MIR) region (4000–500 cm−1), encompassing 13 333–4000 cm−1or 750–2500 nm (nanometers, 10−9 m). NIR and infrared (IR) spectroscopy are routinely used to qualify monomers prior to polymerization reactions. NIR is used to measure the kinetics of polymer onset and can be used to detect end‐point completion and initiator compound levels in polymerization reactions. NIR spectroscopy can also be used to sort polymers and to control the quality of incoming raw monomers and finished polymeric materials. Molecular spectroscopy using the NIR and IR measurement techniques is often used for competitive analysis and to determine thermal or photoinduced oxidation or degradation reactions in polymers. This article delineates the background, theory, band assignments, and applications of NIR spectroscopy for the measurement of polymers and rubbers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".