Gradient‐selected versus phase‐cycled HMBC and HSQC: pros and cons
Bibliographic record
Abstract
Abstract The relative sensitivity of phase‐cycled and gradient‐selected HMBC spectra is assessed. As expected, the gradient‐selected sequence is clearly superior to the phase‐cycled sequence for concentrated solutions where t1 ridges due to incomplete suppression of 1H magnetization bonded to 13C or heteroatoms are the main sources of noise in the phase‐cycled spectrum but are strongly suppressed in the gradient‐selected spectrum. However, the intensity of t1 ridges appears to be directly proportional to signal strength. Consequently, for dilute solutions, t1 ridges often provide only a minor contribution to total noise levels in phase‐cycled HMBC spectra. In this case, provided that one acquires and processes phase‐cycled HMBC spectra in the recommended mode (phase‐sensitive acquisition and mixed‐mode processing), a phase‐cycled HMBC spectrum can show about twice the signal‐to‐noise ratio of an absolute value gradient‐selected HMBC obtained in the same time. More extensive linear prediction is also possible with the phase‐cycled sequence. There are similar advantages to phase‐cycled HSQC spectra over gradient‐selected HSQC spectra. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".