Playing smart vs. playing safe: the joint expression of phenotypic plasticity and potential bet hedging across and within thermal environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptive phenotypic plasticity evolves when cues reliably predict fitness consequences of life-history decisions, whereas bet hedging evolves when environments are unpredictable. These modes of response should be jointly expressed, because environmental variance is composed of both predictable and unpredictable components. However, little attention has been paid to the joint expression of plasticity and bet hedging. Here, I examine the simultaneous expression of plasticity in germination rate and two potential bet-hedging traits - germination fraction and within-season diversification in timing of germination - in seeds from multiple seed families of five geographically distant populations of Lobelia inflata (L.) subjected to a thermal gradient. Populations differ in germination plasticity to temperature, in total germination fraction and in the expression of potential diversification in the timing of germination. The observation of a negative partial correlation between the expression of plasticity and germination variance (potential diversification), and a positive correlation between plasticity and germination fraction is suggestive of a trade-off between modes of response to environmental variance. If the observed correlations are indicative of those between adaptive plasticity and bet hedging, we expect an optimal balance to exist and differ among populations. I discuss the challenges involved in testing whether the balance between plasticity and bet hedging depends on the relative predictability of environmental variance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it