Bibliographic record
Abstract
Hormones regulate most physiological functions and life history from embryonic development to reproduction. In addition to their roles in growth and development, hormones also mediate responses to the abiotic, social and nutritional environments. Hormone signalling is responsive to environmental changes to adjust phenotypes to prevailing conditions. Both hormone levels and receptor densities can change to provide a flexible system of regulation. Endocrine flexibility connects the environment to organismal function, and it is central to understanding environmental impacts and their effect on individuals and populations. Hormones may also act as a 'sensor' to link environmental signals to epigenetic processes and thereby effect phenotypic plasticity within and across generations. Many environmental parameters are now changing in unprecedented ways as a result of human activity. The knowledge base of organism-environmental interactions was established in environments that differ in many ways from current conditions as a result of ongoing human impacts. It is an urgent contemporary challenge to understand how evolved endocrine responses will modulate phenotypes in response to anthropogenic environmental impacts including climate change, light-at-night and chemical pollution. Endocrine responses play a central role in ecology, and their integration into conservation can lead to more effective outcomes. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".