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Record W2134485438 · doi:10.1093/nar/gkl818

WormBase: new content and better access

2006· article· en· W2134485438 on OpenAlexaboutno aff
Tamberlyn Bieri, Darin Blasiar, Philip Ozersky, Igor Antoshechkin, Carol Bastiani, Canaran Payan, Juancarlos Chan, Nansheng Chen, Wen J. Chen, Paul A. Davis, Tristan J. Fiedler, Lisa Girard, Michael Han, Todd Harris, Ranjana Kishore, Raymond Lee, Sheldon McKay, Hans‐Michael Müller, Cecilia Nakamura, Petcherski Andrei, Arun Rangarajan, Anthony S. Rogers, Gary Schindelman, Erich M. Schwarz, Will Spooner, Mary Ann Tuli, Kimberly Van Auken, Daniel Wang, Xiaodong Wang, Gary W. Williams, Richard Durbin, Lincoln Stein, Paul W. Sternberg, John Spieth

Bibliographic record

VenueNucleic Acids Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersNational Human Genome Research InstituteMedical Research Council
KeywordsBiologyCaenorhabditis elegansAnnotationGenomeComputational biologyHigh-content screeningGeneticsGeneCell

Abstract

fetched live from OpenAlex

WormBase (http://wormbase.org), a model organism database for Caenorhabditis elegans and other related nematodes, continues to evolve and expand. Over the past year WormBase has added new data on C.elegans, including data on classical genetics, cell biology and functional genomics; expanded the annotation of closely related nematodes with a new genome browser for Caenorhabditis remanei; and deployed new hardware for stronger performance. Several existing datasets including phenotype descriptions and RNAi experiments have seen a large increase in new content. New datasets such as the C.remanei draft assembly and annotations, the Vancouver Fosmid library and TEC-RED 5' end sites are now available as well. Access to and searching WormBase has become more dependable and flexible via multiple mirror sites and indexing through Google.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.541

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 head0.070
GPT teacher head0.336
Teacher spread0.266 · 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 designBench or experimental
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

Citations93
Published2006
Admission routes1
Has abstractyes

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