Proton spectral editing for discrimination of lactate and threonine 1.31 ppm resonances in human brain in vivo
Bibliographic record
Abstract
A single-voxel proton NMR J-difference editing method for discriminating between the 1.31 ppm resonances of lactate (Lac) and threonine (Thr) in human brain in vivo at 3 T is reported. One double-band and two triple-band Gaussian 180 degrees RF pulses, all with a bandwidth of 15 Hz, were employed within an adiabatic-refocused double-echo localization sequence to induce the target signals of Lac and Thr and simultaneously acquire a creatine singlet in each subscan. The optimum echo time and the editing efficiency were obtained by numerical analysis of the filtering performance. The Lac and Thr signals were extracted, without lipid contamination, from three subspectra. Using the calculated yields, the concentrations of Lac and Thr in the human occipital cortex were estimated to be 0.47+/-0.07 and 0.56+/-0.06 mM (mean+/-SD, N=7), respectively, with reference to Cr at 8 mM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".