Bibliographic record
Abstract
This volume is derived from the Annual Symposium of the British Ecological Society on ‘Speciation and Ecology’ which was held at the University of Sheffield, 28–30 March 2007. The idea for this Symposium arose during a previous meeting in the series, the 2002 ‘Macroecology: Concepts and Consequences’ meeting organized by Tim Blackburn and Kevin Gaston. The 2002 meeting concentrated on large-scale diversity patterns. Many speakers acknowledged the role of speciation in generating diversity and influencing patterns of diversity. Although there was some discussion of the factors that determine rates of speciation, it was striking how little contact there seemed to be between the discipline of macroecology and the large and active field of research into mechanisms of adaptive divergence and speciation. ‘Ecological speciation’ has been an area of research growth in recent years, asking how ecological drivers influence the speciation process. However, the opposite direction of effect, how speciation processes impact on ecological patterns, has been studied less. Therefore, we proposed a meeting whose central objective was to foster dialogue between these two fields. The meeting had an unusual mix of participants but we hope that they managed to communicate effectively with one another! The chapters in this book reflect the range of topics discussed and we hope that they will help to continue the conversations that were started in Sheffield.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.479 | 0.284 |
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".