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Record W2031651733 · doi:10.1371/journal.pcbi.1000094

ISMB 2008 Toronto

2008· article· en· W2031651733 on OpenAlexaboutno aff
Michal Linial, Jill P. Mesirov, B. J. Morrison McKay, Burkhard Rost

Bibliographic record

VenuePLoS Computational Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The International Society for Computational Biology (ISCB) presents the Sixteenth International Conference on Intelligent Systems for Molecular Biology\n(ISMB 2008), to be held in Toronto,\nCanada, July 19–23, 2008. Now in the\nfinal phases of scheduling selected presentations, demonstrations, and posters, the\norganizers are preparing what will likely\nbe recognized as the premier conference\non computational biology in 2008. ISMB\n2008 (http://www.iscb.org/ismb2008/)\nwill follow the road paved by the ISMB/\nECCB 2007 (http://www.iscb.org/\nismbeccb2007/) in Vienna in the attempt\nto specifically encourage increased participation from previously under-represented\ndisciplines of computational biology. This\nconference will feature the best of the\ncomputer and life sciences through a\nvariety of core sessions running in multiple\nparallel tracks, along with single-tracked\nKeynote Presentations, posters on display\nthroughout the duration of the conference,\nand an extensive commercial exposition.\nThe first day (July 18) of the meeting is\nreserved for two-day Special Interest Group\n(SIG) and Satellite meetings, the second day\n(July 19) runs SIGs for the first time in\nparallel with Tutorials and the Student\nCouncil Symposium, and for the first time\ntwo SIGs are running in parallel with the\nmain ISMB meeting (July 20–23)

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.471

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 head0.027
GPT teacher head0.288
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2008
Admission routes1
Has abstractyes

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