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Heredity as Transmission of Information: Butlerian 'Intelligent Design'

2006· article· en· W2052749081 on OpenAlexaff
Donald R. Forsdyke

Bibliographic record

VenueCentaurus · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsHereditySomaInheritance (genetic algorithm)Foundation (evidence)OffspringTransmission (telecommunications)CognitionCognitive scienceComputer scienceBiologyEpistemologyPhilosophyPsychologyHistoryGeneticsNeuroscienceTelecommunications

Abstract

fetched live from OpenAlex

In the 1870s, Ewald Hering and Samuel Butler provided what was, for that time, a scientifically coherent foundation for the Lamarckist view that positive adaptations to the environment acquired during an individual's lifetime can be transmitted to the offspring. Observing that heredity was a form of memory (involving stored information), they distinguished what are now known as genotype and phenotype and proposed that cognitive abilities present in the the most elementary organisms might mediate a transmission of acquired adaptations. While compatible with the then-available facts of evolution, this Butlerian version of 'intelligent design' was rendered less credible by subsequent appreciations of the discrete (discontinuous) inheritance of many phenotypic characters (Mendelism) and of the separation of germ line from soma (Weismanism). However, it can now be seen that 21st-century bioinformatics has 19th-century roots.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.023
Scholarly communication0.0040.007
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations17
Published2006
Admission routes1
Has abstractyes

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