Physiological variation and phenotypic plasticity: a response to ʻPlasticity in arthropod cryotypesʼ by Hawes and Bale
Bibliographic record
Abstract
In a recent publication, Hawes and Bale provide an extended discussion of phenotypic plasticity in the context of low temperature \nresponses of animals. They argue that phenotypic plasticity may be partitioned phylogenetically at several levels and go on to explore these levels, and cold hardiness strategies that they term cryotypes, which in their view constitute cryotypic plasticity. Here we argue that this attempt to partition plasticity is misleading, that the term ʻgenotypic plasticityʼ is potentially highly confusing and a misnomer for physiological variance, and that the term ʻsuperplasticityʼ should not be used. We also show that a definition of strategies as cryotypes is not useful and that the hypothesis about the relationship between evolutionary derivation and extent of plasticity in freeze-avoiding vs freeze-tolerant species is not supported by current evidence.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".