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Record W4389043048 · doi:10.1186/s13100-023-00307-4

Meeting report: transposable elements at the crossroads of evolution, health and disease 2023

2023· article· en· W4389043048 on OpenAlexafffundabout
Irina R. Arkhipova, Kathleen H. Burns, Katherine B. Chiappinelli, Edward B. Chuong, Clément Goubert, Alba Guarné, Amanda M. Larracuente, Eunjung Alice Lee, Henry L. Levin

Bibliographic record

VenueMobile DNA · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsMcGill UniversityMcGill Genome Centre
FundersNational Institute of General Medical SciencesNational Science FoundationNational Institutes of HealthU.S. Department of Health and Human ServicesNational Cancer InstituteCanadian Institutes of Health Research
KeywordsTransposable elementBiologyTheme (computing)RepertoireHost (biology)Diversity (politics)Human geneticsEvolutionary biologyGeneticsGeneSociologyGenomeAnthropology

Abstract

fetched live from OpenAlex

The conference "Transposable Elements at the Crossroads of Evolution, Health and Disease" was hosted by Keystone Symposia in Whistler, British Columbia, Canada, on September 3-6, 2023, and was organized by Kathleen Burns, Harmit Malik and Irina Arkhipova. The central theme of the meeting was the incredible diversity of ways in which transposable elements (TEs) interact with the host, from disrupting the existing genes and pathways to creating novel gene products and expression patterns, enhancing the repertoire of host functions, and ultimately driving host evolution. The meeting was organized into six plenary sessions and two afternoon workshops with a total of 50 invited and contributed talks, two poster sessions, and a career roundtable. The topics ranged from TE roles in normal and pathological processes to restricting and harnessing TE activity based on mechanistic insights gained from genetic, structural, and biochemical studies.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.097
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0970.039

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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
GenreEditorial

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

Citations2
Published2023
Admission routes3
Has abstractyes

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