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One Step Sideways, Three Steps Forward One Woman's Path to Becoming a Biologist

2024· article· en· W6942610680 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsBiologistField (mathematics)Natural selectionNatural (archaeology)Path (computing)Tracking (education)Selection (genetic algorithm)

Abstract

fetched live from OpenAlex

The story of the unorthodox and inspiring life and career of a pioneering biologist Scientist Rosemary Grant's journey in life has involved detours and sidesteps-not the shortest or the straightest of paths, but one that has led her to the top of evolutionary biology. In this engaging and moving book, Grant tells the story of her life and career-from her childhood love of nature in England's Lake District to an undergraduate education at the University of Edinburgh through a swerve to Canada and teaching, followed by marriage, children, a PhD at age forty-nine, and her life's work with Darwin's finches in the Galápagos islands. Grant's unorthodox career is one woman's solution to the problem of combining professional life as a field biologist with raising a family.Grant describes her youthful interest in fossils, which inspired her to imagine another world, distant yet connected in time-and which anticipated her later work in evolutionary biology. She and her husband, Peter Grant, visited the Galápagos archipelago annually for forty years, tracking the fates of the finches on the small, uninhabited island of Daphne Major. Their work has profoundly altered our understanding of how a group of eighteen species has diversified from a single ancestral species, demonstrating that evolution by natural selection can be observed and interpreted in an entirely natural environment. Grant's story shows the rewards of following a winding path and the joy of working closely with a partner, sharing ideas, disappointments, and successes

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.051
GPT teacher head0.225
Teacher spread0.174 · 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.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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