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Record W1948350817 · doi:10.1139/gen-2015-0107

Limits of imagination: the 150th Anniversary of Mendel’s Laws, and why Mendel failed to see the importance of his discovery for Darwin’s theory of evolution

2015· article· en· W1948350817 on OpenAlexaffvenue
Rama S. Singh

Bibliographic record

VenueGenome · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHeredityDarwin (ADL)GeniusMendelian inheritanceEpistemologyInheritance (genetic algorithm)Natural selectionPhilosophyLawSociologyPopulationBiologyHistoryGeneticsArt historyComputer science

Abstract

fetched live from OpenAlex

Mendel is credited for discovering Laws of Heredity, but his work has come under criticism on three grounds: for possible falsification of data to fit his expectations, for getting undue credit for the laws of heredity without having ideas of segregation and independent assortment, and for being interested in the development of hybrids rather than in the laws of heredity. I present a brief review of these criticisms and conclude that Mendel deserved to be called the father of genetics even if he may not, and most likely did not, have clear ideas of segregation and particulate determiners as we know them now. I argue that neither Mendel understood the evolutionary significance of his findings for the problem of genetic variation, nor would Darwin have understood their significance had he read Mendel's paper. I argue that the limits to imagination, in both cases, came from their mental framework being shaped by existing paradigms-blending inheritance in the case of Darwin, hybrid development in the case of Mendel. Like Einstein, Darwin's natural selection was deterministic; like Niels Bohr, Mendel's Laws were probabilistic-based on random segregation of trait-determining "factors". Unlike Einstein who understood quantum mechanics, Darwin would have been at a loss with Mendel's paper with no guide to turn to. Geniuses in their imaginations are like heat-seeking missiles locked-in with their targets of deep interests and they generally see things in one dimension only. Imagination has limits; unaided imagination is like a bird without wings--it goes nowhere.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

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

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 head0.062
GPT teacher head0.236
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.

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

Citations11
Published2015
Admission routes2
Has abstractyes

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