Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 −1 or 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.108 | 0.000 |
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 it