A Beginner's Guide to Nutritional Profiling in Physiology and Ecology
Bibliographic record
Abstract
The nutritional history of an organism is often difficult to ascertain. Nonetheless, this information on past diet can be particularly important when explaining the role of nutrition in physiological responses and ecological dynamics. One approach to infer the past dietary history of an individual is through characterization of its nutritional phenotype, an interrelated set of molecular and physiological properties that are sensitive to dietary stress. Comparisons of nutritional phenotypes between a study organism and reference phenotypes have the potential to provide insight into the type and intensity of past dietary constraints. Here, we describe this process of nutritional profiling for ecophysiological research in which a suite of molecular and physiological responses are cataloged for animals experiencing known types and intensities of dietary stress and are quantitatively compared with those of unknown individuals. We supplement this delineation of the process of nutritional profiling with a first-order analysis of its sensitivity to the number of response variables in the reference database, their responsiveness to diet, and the size of reference populations. In doing so, we demonstrate the considerable promise this approach has to transform future studies of nutrition by its ability to provide more and better information on responses to dietary stress in animals and their populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.093 |
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".