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Record W1562226338 · doi:10.3968/4164

Nonlinear Genetics Inbreeding and Genetic Load

2013· article· en· W1562226338 on OpenAlexvenueno aff
Volobuev Andrey Nikolaevich, Romanchuk Peter Ivanovich, Malishev Vladimir Kirikovich

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsInbreedingGenetic loadPopulationPopulation geneticsNonlinear systemBiologyGenetic modelGeneticsPhysicsDemographySociology

Abstract

fetched live from OpenAlex

Comparison of concepts of occurrence and accumulation of the genetic load in result of inbreeding for a family tree and a population is given. It is shown that for the population the analysis of the genetic load concerns to area of nonlinear genetics. The nonlinear differential equation of the second order being the special case of the Hardy-Weinberg law at presence inbreeding for the population is found. The numerical decision of this equation allowed to find the law of increase of recessive allele frequency in the population is given. Some aspects of the traditional theory of the genetic load are considered.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.268
Teacher spread0.261 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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