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Record W2091845681 · doi:10.1086/676588

Conceptual and Empirical Challenges of Ascribing Functions to Transposable Elements

2014· article· en· W2091845681 on OpenAlexafffund
Tyler A. Elliott, Stefan Linquist, T. Ryan Gregory

Bibliographic record

VenueThe American Naturalist · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsENCODETransposable elementOrganismRelevance (law)Function (biology)GenomeBiologySelection (genetic algorithm)EpistemologyGeneticsComputer sciencePhilosophyPolitical scienceLawGene

Abstract

fetched live from OpenAlex

Media attention and the subsequent scientific backlash engendered by the claim by spokespeople for the Encyclopedia of DNA Elements (ENCODE) project that 80% of the human genome has a biochemical function highlight the need for a clearer understanding of function concepts in biology. This article provides an overview of two major function concepts that have been developed in the philosophy of science--the causal role concept and the selected effects concept--and their relevance to ENCODE. Unlike in some previous critiques, the ENCODE project is not considered problematic here because it employed a causal role definition of function (which is relatively common in genetics) but because of how this concept was misused. In addition, several unique challenges that arise when dealing with transposable elements (TEs) but that were ignored by ENCODE are highlighted. These include issues surrounding TE-level versus organism-level selection, the origins versus the persistence of elements, and accidental versus functional organism-level benefits. Finally, some key questions are presented that should be addressed in any study aiming to ascribe functions to major portions of large eukaryotic genomes, the majorities of which are made up of transposable elements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.260
Teacher spread0.224 · 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 designObservational
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

Citations35
Published2014
Admission routes2
Has abstractyes

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