Categorizing and qualifying nutritional lipidomic data
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lipidomic profiling of biological samples is increasing in nutritional research applications. 'Lipidomic analyses' however can be quite variable in specific methods and the type of information about the specific lipids that is revealed. The lack of defined and simple terminology to describe aspects of lipidomics presents a challenge in the use of lipidomics across interdisciplinary research groups. RECENT FINDINGS: We propose the use of macrolipidomics and microlipidomics to define lipidomic strategies based on analytical outcomes. Macrolipidomics involves the global characterization of the most abundant lipids in a system, whereas microlipidomics examines low abundant lipids with potent bioactivity that typically require specialized analyses. We also propose that in addition to the term 'brutto', the terms 'medio, genio, and infinio' be used to indicate when information about the lipid molecule increases from isobars/isomers to regio-isomers with carbon-carbon double bond information. SUMMARY: The use of these terms will help establish a common language around the field of lipidomics and improve communication and uptake in the field of clinical nutrition. Macrolipidomic and microlipidomic terms quickly convey the general purpose of the approach. Brutto, medio, genio, and infino quickly convey the nature of the lipid identification.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".