Signal and Time Resolved Information Experiment (SATIRE): An NMR Supersequence for Monitoring Complex Environmental and Biological Processes without <sup>13</sup> C Enrichment
Bibliographic record
Abstract
Complex NMR process monitoring often requires chemical shift dispersion afforded by 2D NMR and high temporal resolution of 1 H NMR in the same experiment. However, interleaving conventional 1D and 2D experiments leaves “time gaps” in each data set. This work presents the Signal And TIme Resolved information Experiment (SATIRE), an NMR supersequence that uses the leftover 1 H magnetization (98.9%) not used in the HSQC to collect 1 H NMR data within the same scan, using a single receiver. The approach is designed for those interested in monitoring complex systems that have a low signal-to-noise ratio (SNR). For example, if a researcher monitored living organisms for 24 h by 1 H NMR alone there may not be enough spectral dispersion for assignment. The low SNR would necessitate a long HSQC, potentially doubling the required NMR time. Conversely, with SATIRE the HSQC would be collected essentially for “free” at the same time as the 1 H data are collected. In SATIRE, the signal-to-noise ratios of the HSQC and 1 H are identical to the standalone experiments and are designed to permit relative quantitation in the 1 H data. Because the data are collected as a pseudo-3D experiment, it is also possible to extract individual shorter time scale HSQC or 1 H NMR spectra around time points of interest, SNR permitting. The approach is introduced and then demonstrated on benchtop NMR to monitor sucrose hydrolysis and finally at high field (500 MHz) to follow anoxic stress in vivo . In summary, SATIRE allows simultaneous acquisition of 1D and 2D NMR without compromising either data set and supports complex process monitoring in any application at natural abundance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".