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In the light of evolution: essays from the laboratory and field Jonathan Losos, eds 2011. Roberts and Company Publishers

2011· article· en· W2054469098 on OpenAlexaff
Michelle Tseng

Bibliographic record

VenueEvolutionary Applications · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFavouriteReading (process)CharismaField (mathematics)Art historyBiologyMedia studiesSociologyHistoryPhilosophyLawPolitical scienceLinguisticsMathematics

Abstract

fetched live from OpenAlex

In the light of evolution: essays from the laboratory and field Jonathan Losos , eds 2011 . Roberts and Company Publishers My job requires me to read and critique papers in evolutionary biology almost every day, so it’s understandable to say that seldom do I want to spend my evenings reading more evolutionary biology. This book is the rare exception. In ‘In the light of evolution…’, Jonathan Losos has assembled a remarkably accessible, highly readable, informative and entertaining series of 17 essays by prominent evolutionary biologists, historians, science writers and paleontologists. Most essays include background on how the author became interested in their field, a discussion of the main findings of their work so far, and a summary of where they expect their research to go in the future. However, while the reader is given a comprehensive overview of several key study systems in evolutionary biology, what’s most impressive in this book is how the essays are delivered. Most of the chapters are so captivating that you find yourself wanting them not to end. My favourite four or five essays made me feel that I’d just come home from a delightful chat with the authors over drinks. Of course, not all of the essays leave you breathless, in fact some are downright stilted and you’d probably be better off reading the primary literature on the same topic. The biggest omission was the complete absence of essays by botanists. Surely charismatic ‘plant’ people like Spencer ‘swashbuckler’ Barrett, Loren ‘Michelangelo’ Rieseberg, and Doug ‘rock star’ Schemske could all have made wonderful contributions to this volume. However, the pros far outweigh the cons in this case and Losos has done a wonderful job putting together a book that’s sure to be found on the night tables of both armchair and seasoned scientists alike.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.207
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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