Testing Inferences in Developmental Evolution: The Forensic Evidence Principle
Bibliographic record
Abstract
Developmental evolution (DE) examines the influence of developmental mechanisms on biological evolution. Here we consider the question: "what is the evidence that allows us to decide whether a certain developmental scenario for an evolutionary change is in fact "correct" or at least falsifiable?" We argue that the comparative method linked with what we call the "forensic evidence principle" (FEP) is sufficient to conduct rigorous tests of DE scenarios. The FEP states that different genetically mediated developmental causes of an evolutionary transformation will leave different signatures in the development of the derived character. Although similar inference rules have been used in practically every empirical science, we expand this approach here in two ways: (1) we justify the validity of this principle with reference to a well-known result from mathematical physics, known as the symmetry principle, and (2) propose a specific form of the FEP for DE: given two or more developmental explanations for a certain evolutionary event, say an evolutionary novelty, then the evidence discriminating between these hypotheses will be found in the most proximal internal drivers of the derived character. Hence, a detailed description of the ancestral and derived states, and their most proximal developmental drivers are necessary to discriminate between various evolutionary developmental hypotheses. We discuss how this stepwise order of testing is necessary, establishes a formal test, and how skipping this order of examination may violate a more accurate examination of DE. We illustrate the approach with an example from avian digit evolution.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".