Essential Terminology Connects NMR and qNMR Spectroscopy to Its Theoretical Foundation
Bibliographic record
Abstract
Classical 1D 1H NMR spectra are prototypic for NMR spectroscopy in that they represent a wealth of chemical information encoded into convoluted graphs or patterns that contain complex features (aka multiplets), even for seemingly simple molecules. Accordingly, the utility of NMR depends on the theoretical and visual skills required to extract all the physical parameters that represent usable structural and quantitative information. Moreover, it depends on the ability of the analyst to communicate them effectively and reproducibly. After decades of continuous development, NMR spectroscopy has reached a stage where its analytical capabilities have outgrown the typical level of detail of interpretation, especially of 1D NMR spectra. The quantum-mechanical (QM) foundation, history, evolution, and (in-)consistency of widely applied terminology calls for re-examination and recalibration. In order to develop new perspectives on solution-state NMR analysis, including the rapidly evolving quantitative NMR (qNMR), the present study draws on the well-established NMR model systems and molecules (AB2C2, strychnine, testosterone, α-santonin). Through well-documented key topics related to spectral acquisition and analysis, the study builds the foundation for a modular, coherent, and standardized nomenclature of NMR terminology. This is a necessary condition for a healthy research data lifecycle including their management and reuse. This work presents experimental evidence and connects with essential concepts of QM theory that clarify the distinct meaning of the primary terms: resonance, signal, pattern, peak, line, transition; as well as other widely used terms: splitting, multiplicity/multiplet, resolution, and dispersion. The proposed NMR terminology was built through a consensus-finding process that evolved from extended pharmacopoeial and research coordination efforts. It is supported by detailed figures and NMR data interpretation that employs QM-based full spin analysis.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".