Individual variation in endocrine systems: moving beyond the ‘tyranny of the Golden Mean’
Bibliographic record
Abstract
Twenty years ago, Albert Bennett published a paper in the influential book New directions in ecological physiology arguing that individual variation was an 'underutilized resource'. In this paper, I review our state of knowledge of the magnitude, mechanisms and functional significance of phenotypic variation, plasticity and flexibility in endocrine systems, and argue for a renewed focus on inter-individual variability. This will provide challenges to conventional wisdom in endocrinology itself, e.g. re-evaluation of relatively simple, but unresolved questions such as structure-function relationships among hormones, binding globulins and receptors, and the functional significance of absolute versus relative hormone titres. However, there are also abundant opportunities for endocrinologists to contribute solid mechanistic understanding to key questions in evolutionary biology, e.g. how endocrine regulation is involved in evolution of complex suites of traits, or how hormone pleiotropy regulates trade-offs among life-history traits. This will require endocrinologists to embrace the raw material of adaptation (heritable, individual variation and phenotypic plasticity) and to take advantage of conceptual approaches widely used in evolutionary biology (selection studies, reaction norms, concepts of evolutionary design) as well as a more explicit focus on the endocrine basis of life-history traits that are of primary interest to evolutionary biologists (cf. behavioural endocrinology).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".