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Record W2584716309 · doi:10.1609/aimag.v32i1.2338

Reports of the AAAI 2010 Fall Symposia

2011· article· en· W2584716309 on OpenAlexaff
Roger Azevedo, Gautam Biswas, Dan Bohus, Ted Carmichael, Mark A. Finlayson, Mirsad Hadžikadić, Catherine Havasi, Eric Horvitz, Takayuki Kanda, Oluwasanmi Koyejo, William F. Lawless, Doug Lenat, Felipe Meneguzzi, Bilge Mutlu, Jean Oh, Roberto Pirrone, Antoine Raux, Donald Sofge, Gita Sukthankar, Benjamin Van Durme, Neil Yorke‐Smith

Bibliographic record

VenueAI Magazine · 2011
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsDialog boxThursdayComputer scienceGateway (web page)Commonsense knowledgeArtificial intelligenceCognitive scienceWorld Wide WebPsychologyKnowledge representation and reasoning

Abstract

fetched live from OpenAlex

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11–13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

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)0.007
metaresearch head score (Gemma)0.007
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: Other · Consensus signal: none
Teacher disagreement score0.119
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0080.005
Open science0.0020.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.1190.081

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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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
GenreOther

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

Citations1
Published2011
Admission routes1
Has abstractyes

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