In search of evolutionary developmental mechanisms: The 30‐year gap between 1944 and 1974
Bibliographic record
Abstract
The approach I have elected in this retrospective of how I became a student of evo-devo is both biographical and historical, a case study along the lines of Waddington's The Evolution of an Evolutionist ('75), although in my case it is the Evolution of an Evo-devoist. What were the major events that brought me to developmental biology and from there to evo-devo? They were, of course, specific to my generation, to the state of knowledge at the time, and to my own particular circumstances. Although exposed to evolution and embryology as an undergraduate in the 1960s, my PhD and post-PhD research programme lay within developmental biology until the early 1970s. An important formative influence on my studies as an undergraduate was the work of Conrad Hal Waddington (1905-1975), whose writings made me aware of genetic assimilation and gave me an epigenetic approach to my developmental studies. The switch to evo-devo (and my discovery of the existence of the neural crest), I owe to an ASZ (now SICB) symposium held in 1973.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".