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Record W4393830490 · doi:10.5281/zenodo.3354307

Critical Assessment of automated Structure Determination of Proteins by NMR

2015· dataset· en· W4393830490 on OpenAlexaff
Antonio Rosato, Wim Vranken, Rasmus H. Fogh, Timothy J. Ragan, Roberto Tejero, Kari Pederson, Hsiau‐Wei Lee, James H. Prestegards, Adelinda Yee, Bin Wu, Alexander Lemak, Scott Houliston, C.H. Arrowsmith, Michael A. Kennedy, Thomas R. Acton, Rong Xiao, Gahoua Liu, G.T. Montelione, Geerten W. Vuister

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2015
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
FundersEuropean Commission
KeywordsChemistryComputer scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

The community-wide initiative "Critical Assessment of Automated Structure Determination of Proteins by NMR (CASD-NMR)" was launched in 2009 to to evaluate the ability of automated methods to produce 3D protein structures from NMR data that closely match structures manually determined by experts. This dataset includes all the experimental data made available to the participants of CASD-NMR in the two completed rounds of the initiative. Also refer to http://www-nmr.cabm.rutgers.edu/blindtest/blind.html for additional details, including first release date and link to each final PDB entry

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)0.014
metaresearch head score (Gemma)0.033
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.004
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0060.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.018

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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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