Focusing on “the important” through targeted NMR experiments: an example of selective<sup>13</sup>C–<sup>12</sup>C bond detection in complex mixtures
Bibliographic record
Abstract
Current research is attempting to address more complex questions than ever before. As such, the need to follow complex processes in intact media and mixtures is becoming commonplace. Here, a targeted NMR experiment is introduced which selectively detects the formation of 13C-12C bonds in mixtures. This study introduces the experiment on simple standards, and then demonstrates the potential on increasingly complex processes including: fermentation, Arabidopsis thaliana germination/early growth, and metabolism in Daphnia magna both ex vivo and in vivo. As signals from the intact 12C and 13C pools are themselves filtered out, correlations are only observed when a component from each pool combines (i.e. new 13C-12C bonds) in the formation of new structures. This targeted approach significantly reduces the complexity of the mixtures and provides information on the fate and reactivity of carbon in environmental and biological processes. The experiment has application to follow bond formation wherever two pools of carbon are brought together, be it the incorporation of 13C enriched food into a living organism's biomass, or the degradation of 13C enriched plant material in soil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".