Overcoming maladaptive plasticity through plastic compensation
Bibliographic record
Abstract
Abstract Most species evolve within fluctuating environments, and have developed adaptations to meet the challenges posed by environmental heterogeneity. One such adaptation is phenotypic plasticity, or the ability of a single genotype to produce multiple environmentally-induced phenotypes. Yet, not all plasticity is adaptive. Despite the renewed interest in adaptive phenotypic plasticity and its consequences for evolution, much less is known about maladaptive plasticity. However, maladaptive plasticity is likely an important driver of phenotypic similarity among populations living in different environments. This paper traces four strategies for overcoming maladaptive plasticity that result in phenotypic similarity, two of which involve genetic changes (standing genetic variation, genetic compensation) and two of which do not (standing epigenetic variation, plastic compensation). Plastic compensation is defined as adaptive plasticity overcoming maladaptive plasticity. In particular, plastic compensation may increase the likelihood of genetic compensation by facilitating population persistence. We provide key terms to disentangle these aspects of phenotypic plasticity and introduce examples to reinforce the potential importance of plastic compensation for understanding evolutionary change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".