DREAMTIME NMR Spectroscopy: Targeted Multi‐Compound Selection with Improved Detection Limits
Bibliographic record
Abstract
Abstract NMR/MRI are critical tools for studying molecular structure and interactions but suffer from relatively low sensitivity and spectral overlap. Here, a Nuclear Magnetic Resonance (NMR) approach, termed DREAMTIME, is introduced that provides “a molecular window” inside complex systems, capable of showing only what the user desires, with complete molecular specificity. The user chooses a list of molecules of interest, and the approach detects only those targets while all other molecules are invisible. The approach is demonstrated in whole human blood and urine, small living aquatic organisms in 1D/2D NMR, and MRI. Finally, as proof‐of‐concept, once overlap is removed via DREAMTIME, a novel “multi‐focusing” approach can be used to increase sensitivity. In human blood and urine, sensitivity increases of 7–12 fold over standard 1H NMR are observed. Applicable even to unknowns, DREAMTIME has widespread application, from monitoring product formation in organic chemistry to monitoring/identifying suites of molecular targets in complex media or in vivo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".