Palaeontology and Evolutionary Developmental Biology: A Science of the Nineteenth and Twenty–first Centuries
Bibliographic record
Abstract
A wind of change has swept through palaeontology in the past few decades. Contrast Sir Peter Medawar’s dismissive: ‘palaeontology is a particularly undemanding branch of science’ (as recalled by John Maynard Smith in Sabbagh 1999, p. 158) with ‘Palaeontology: grasping the opportunities in the science of the twenty–first century’, the title of a contribution to a special issue of Geobios by the Cambridge palaeontologist, Simon Conway Morris (1998a). The winds of change have come partly from palaeontologists seeking to broaden the impact of their studies and partly from biologists (neontologists) realizing the contributions that palaeontology can make to their disciplines. Consequently, impressions of past life preserved in stone are coming alive. Fossils are being described and analyzed using new tools and languages as the static fossil record becomes a record of transitions in patterns that can be explained and related to biological, ecological, climatic and tectonic changes. The latest addition is evolutionary developmental biology, or ‘evo–devo’, whose language provides a new basis upon which to interpret anatomical change, both materially and mechanistically. In this review I examine the major contributions made by palaeontology, how palaeontology has been linked to evolution and to embryology in the past, and how links with evo–devo have enlivened and will continue to enliven both palaeontology and evo–devo. Closer links between the two fields should illuminate important unresolved issues related to the origin of the metazoans (e.g. Why is there a conflict between molecular clocks and the fossil record in timing the metazoan radiation; were Precambrian metazoan ancestors similar to extant larvae or to miniature adults?) and to diversification of the metazoans (e.g. How do developmental constraints bias the direction of evolution; how do microevolutionary developmental processes relate to macroevolutionary changes?).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".