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Costs and benefits of genetic heterogeneity within organisms

2004· review· en· W2062763438 on OpenAlexaff
Mario Pineda‐Krch, Kari Lehtilä

Bibliographic record

VenueJournal of Evolutionary Biology · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of British Columbia
FundersRoyal Society
KeywordsBiologyOrganismGenetic heterogeneityEvolutionary biologySelection (genetic algorithm)GeneticsHomogeneousGenetic variationPhenotypeGene

Abstract

fetched live from OpenAlex

An increasing number of studies have recently detected within-organism genetic heterogeneity suggesting that genetically homogeneous organisms may be rare. In this review, we examine the potential costs and benefits of such intraorganismal genetic heterogeneity (IGH) on the fitness of the individual. The costs of IGH include cancerous growth, parasitism, competitive interactions and developmental instability, all of which threaten the integrity of the individual while the potential benefits are increased genetic variability, size-specific processes, and synergistic interactions between genetic variants. The particular cost or benefit of IGH in a specific case depends on the organism type and the origin of the IGH. While mosaicism easily arise by genetic changes in an individual, and will be the more common type of IGH, chimerism originates by the fusion of genetically distinct entities, and is expected to be substantially rare in most organisms. Potential conflicts and synergistic effects between different genetic lineages within an individual provide an interesting example for theoretical and empirical studies of multilevel selection.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations128
Published2004
Admission routes1
Has abstractyes

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