Comparison of the computational NMR chemical shifts of choline with the experimental data
Bibliographic record
Abstract
One of the main biological markers of the presence of cancer in living patients is an over-expression of total choline (tCho), which is the sum of free choline and its derivatives. 1 H Magnetic Resonance Spectroscopy, or H-MRS, enables the quantification of tCho via its proton spectra, and thus has the potential to be a diagnostic tool for the presence of cancer and an accurate early indicator of the response of cancer to treatment. However, it remains difficult to quantify individual choline derivatives, since they share a large structural similarity ((CH 3 ) 3 -N + -CH 2 -CH 2 -O-), of which the strongest signal detectable by MRS is that of the choline "head group": the three methyl groups bonded to the nitrogen. This work used ACENet, a high performance computing system, to attempt to model the NMR parameters of choline derivatives, with the focus of this report being free choline. Optimized structures were determined using Density Functional Theory and the B3LYP electron correlation functional. The Polarizable Continuum Model was used to evaluate solvent effects. The Gauge-Invariant Atomic Orbital method was found to be the superior method for calculating the NMR parameters of cholines.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".