Bibliographic record
Abstract
Kassen’s book lets us see evolution, not as a dusty subject of the past, but as a vibrant, experimental science that shapes how we understand everything from diseases to biodiversity. Sarah Otto, Professor of Zoology & University Killam Professor, The University of British Colombia, Canada I didn’t think it was possible, but this massively updated examination of experimental evolution is even better than the first edition. Rees Kassen has written a spectacular synthesis of one of the most exciting areas in science today. A must read for any evolutionary biologist. Jonathan Losos, Professor of Biology and Director of the Living Earth Collaborative, Washington University in St Louis, USA In the 2nd edition of Experimental Evolution and the Nature of Biodiversity, Rees Kassen successfully improves on a classic, by offering a text that emphasizes technical approaches to experimental evolution, and connecting it to central canon in evolutionary theory. In doing so, Kassen has opened the door to not only teaching how experimental evolution can improve our understanding of evolution, but also why it is an essential tool for observing evolution-in-action, and identifying the mechanistic underpinnings that underlie adaptations. This is rich and scholarly work, and essential reading for anyone who seeks to grasp a powerful tool for revealing how evolution happens in the natural world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.791 | 0.700 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".