Macrophysiology: A Conceptual Reunification
Bibliographic record
Abstract
Widespread recognition of the importance of biological studies at large spatial and temporal scales, particularly in the face of many of the most pressing issues facing humanity, has fueled the argument that there is a need to reinvigorate such studies in physiological ecology through the establishment of a macrophysiology. Following a period when the fields of ecology and physiological ecology had been regarded as largely synonymous, studies of this kind were relatively commonplace in the first half of the twentieth century. However, such large-scale work subsequently became rather scarce as physiological studies concentrated on the biochemical and molecular mechanisms underlying the capacities and tolerances of species. In some sense, macrophysiology is thus an attempt at a conceptual reunification. In this article, we provide a conceptual framework for the continued development of macrophysiology. We subdivide this framework into three major components: the establishment of macrophysiological patterns, determining the form of those patterns (the very general ways in which they are shaped), and understanding the mechanisms that give rise to them. We suggest ways in which each of these components could be developed usefully.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.060 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".