Minimal-Risk Seed Heteromorphism: Proportions of Seed Morphs for Optimal Risk-Averse Heteromorphic Strategies
Why this work is in the frame
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Bibliographic record
Abstract
Seed heteromorphism is the reproductive strategy characterized by the simultaneous production of multiple seed types. While comparing heteromorphic to monomorphic strategies is mathematically simple, there is no explicit test for assessing which ratio of seed morphs minimizes fitness variance, and hence offers a basis for comparing different heteromorphic strategies. Such a test may be particularly valuable when more than two distinct morphs are present, since many strategies may have equivalent geometric fitnesses. As noted by Gillespie (1974), in these cases avoiding rare but evolutionarily important instances of severe reductions in fitness involves the minimization of variation in fitness-i.e., risk. Here I compute the optimal proportions of two or more seed morphs for heteromorphic strategies that either: (1) minimize total fitness variance; or (2) maximize the fitness-risk ratio-i.e., the "extra" fitness accrued per unit of "extra" fitness variance. This work thereby provides a testable null hypothesis to estimate the optimal frequencies of seed morphs when multiple heteromorphic strategies have evolved in environments with severe fitness risks. Moreover, it also permits the calculation of expected seed morph frequencies when more than two seed morphs are produced.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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