Integrating Ecological and Evolutionary Context in the Study of Maternal Stress
Bibliographic record
Abstract
Maternal stress can prenatally influence offspring phenotypes and there are an increasing number of ecological studies that are bringing to bear biomedical findings to natural systems. This is resulting in a shift from the perspective that maternal stress is unanimously costly, to one in which maternal stress may be beneficial to offspring. However, this adaptive perspective is in its infancy with much progress to still be made in understanding the role of maternal stress in natural systems. Our aim is to emphasize the importance of the ecological and evolutionary context within which adaptive hypotheses of maternal stress can be evaluated. We present five primary research areas where we think future research can make substantial progress: (1) understanding maternal and offspring control mechanisms that modulate exposure between maternal stress and subsequent offspring phenotype response; (2) understanding the dynamic nature of the interaction between mothers and their environment; (3) integrating offspring phenotypic responses and measuring both maternal and offspring fitness outcomes under real-life (either free-living or semi-natural) conditions; (4) empirically testing these fitness outcomes across relevant spatial and temporal environmental contexts (both pre- and post-natal environments); (5) examining the role of maternal stress effects in human-altered environments-i.e., do they limit or enhance fitness. To make progress, it is critical to understand the role of maternal stress in an ecological context and to do that, we must integrate across physiology, behavior, genetics, and evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".