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Record W1550982495 · doi:10.1109/asonam.2013.6785663

Welcome from the ASONAM 2013 program chairs

2013· article· en· W1550982495 on OpenAlexaffabout
Tansel Özyer, Peter J. Carrington, Ee‐Peng Lim

Bibliographic record

VenueAdvances in Social Networks Analysis and Mining · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHospitalityLibrary scienceTourismOperations researchMedia studiesPolitical scienceEngineeringSociologyComputer scienceLaw

Abstract

fetched live from OpenAlex

As program committee chairs of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, held at Sheraton on the Falls in Niagara Falls, Ontario from August 25 to August 28, 2013, and on behalf of the members of the organizing committee and members of the technical program committee we would like to welcome you and wish you will enjoy every moment of your stay in Niagara Falls, whether attending the sessions or sightseeing during your free time. The ASONAM conference series bring together researchers from around the world to share the latest advances in the emerging and attractive field of Social Networks Analysis and Mining. The ASONAM conference series provide a single, high-profile forum for research in the theory and practice of social networks analysis and mining. It was initiated in 2009 in Athens at the Hellenic American University. ASONAM 2010, the second of the series, was held at the University of Southern Denmark, Odense, Denmark followed by ASONAM 2011, in Kaohsiung, Taiwan National University of Kaohsiung, and we had another interesting gathering in Istanbul in August 2012 who enjoyed an outstanding ASONAM 2012 from the rich and informative scientific program, to the organization and hospitality, to the uniqueness of the city connecting two continents. Fortunately, ASONAM 2013 is not less important in terms of the venue which stands on the fascinating area of Niagara Falls at the connection between Canada and the United States.

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 score0.542
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.281
Teacher spread0.273 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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