The importance of heritability estimates for understanding the evolution of cognition: a response to comments on Croston et al.
Bibliographic record
Abstract
We agree with all 3 sets of commentators ( Healy 2015 ; Smulders 2015 ; Thornton and Wilson 2015 ) that the goal we set is challenging, ( Croston et al. 2015 ) and we are well aware of the magnitude of effort involved in such an undertaking. It is notable that 50 years ago, Sydney Brenner wisely chose the simple nematode, Caenorhabditis elegans , as a model system for dissecting the links among genes, neurons, and behavior ( Brenner 1974 ), yet hundreds of thousands of published papers and a few Nobel prizes later, there is still no end in sight. Given that C. elegans are a relatively simple organism and that cognition in most species is a complex phenotype associated with many genes of small effects, quantitative genetic approaches will likely provide the most useful advances in understanding how natural selection affects cognition. We are happy to hear that there is support for a renewed focus on trait heritability in behavioral ecology research, and we look forward to further insight arising from this effort.
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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 | 0.000 |
| 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 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".