How the 1D‐NOESY suppresses solvent signal in metabonomics NMR spectroscopy: An examination of the pulse sequence components and evolution
Bibliographic record
Abstract
Abstract Metabonomics has become an increasingly shared pursuit in international research. Presently the two most common techniques are mass spectrometry and nuclear magnetic resonance (NMR) spectrometry either in isolation or in conjunction. The 1D‐1H‐NOESY is the most utilized (Beckonert et al., Nat Protoc 2007;2:2692–2703) NMR pulse sequence for the collection of metabonomics NMR data from biological samples such as blood plasma, serum, urine, cerebrospinal fluid, sputum, or homogenized tissue extracts. While the 1D version of the 2D‐1H,1H‐NOESY pulse sequence has become widely used in metabonomics studies, the understanding of the mechanism of solvent suppression has not kept pace. This article will examine the mechanisms by which the 1D‐NOESY suppresses solvent signals and detail the pulse sequence's components in terms of function, phase cycle, and performance. © 2011 Wiley Periodicals, Inc. Concepts Magn Reson Part A 38: 197–220, 2011.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".