The sleeping giant needs coffee: overlooked areas for the integration of plant ecophysiology and evolutionary biology
Bibliographic record
Abstract
Interpretations of evolutionary outcomes are limited without incorporation of physiological ecology; and ecophysiological interpretations would benefit from incorporating evolutionary perspectives. Although there has been a rise of studies in the last 20 years between these fields, evolutionary studies that incorporate plant physiology have largely focused on the same traits (i.e., flowering time, specific leaf area, etc.), neglecting to incorporate cellular and developmental traits. This is largely due to the high throughput demands in evolutionary studies and the lack of technological advancements in ecophysiology. However, this bias in measured traits has resulted in limiting our understanding of plant form and function evolution. On the other hand, most detailed studies on plant physiological and anatomical responses to the environment are either in applied sciences, focused on economically important plants, or examine model organisms rather than wild populations. These detailed ecophysiological studies generally do not incorporate evolutionary discourse, even though they often study adaptation. The aim of this paper is to offer a comprehensive resource, building upon previous works, for researchers to bridge the gap between ecophysiology and evolutionary ecology.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".