Minimally invasive biomarkers in human and non‐human primate evolutionary biology: Tools for understanding variation and adaptation
Bibliographic record
Abstract
BACKGROUND: The use of minimally invasive biomarkers (MIBs - physiological biomarkers obtained from minimally invasive sample types) has expanded rapidly in science and medicine over the past several decades. The MIB approach is a methodological strength in the field of human and non-human primate evolutionary biology (HEB). Among humans and our closest relatives, MIBs provide unique opportunities to document phenotypic variation and to operationalize evolutionary hypotheses. AIMS: This paper overviews the use of MIBs in HEB. Our objectives are to (1) highlight key research topics which successfully implement MIBs, (2) identify promising yet under-investigated areas of MIB application, and (3) discuss current challenges in MIB research, with suggestions for advancing the field. DISCUSSION AND CONCLUSIONS: A range of MIBs are used to investigate focal topics in HEB, including energetics and life history variation/evolution, developmental plasticity, and social status and dominance relationships. Nonetheless, we identify gaps in existing MIB research on traits such as physical growth and gut function that are central to the field. Several challenges remain for HEB research using MIBs, including the need for additional biomarkers and methods of assessment, robust validations, and approaches that are standardized across labs and research groups. Importantly, researchers must provide better support for adaptation and fitness effects in hypothesis testing (e.g., by obtaining complementary measures of energy expenditure, demonstrating redundancy of function, and performing lifetime/longitudinal analyses). We point to continued progress in the use of MIBs in HEB to better understand the past, present, and future of humans and our closest primate relatives.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".