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Record W1795525306 · doi:10.1609/aimag.v34i4.2511

The AAAI‐13 Conference Workshops

2013· article· en· W1795525306 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAI Magazine · 2013
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsArtificial intelligenceContext (archaeology)Computer sciencePersonalizationRoboticsApplications of artificial intelligenceArtificial intelligence, situated approachWorld Wide WebRobot

Abstract

fetched live from OpenAlex

The AAAI‐13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context‐Aware System Architectures (WS‐13‐05); Artificial Intelligence and Robotics Methods in Computational Biology (WS‐13‐06); Combining Constraint Solving with Mining and Learning (WS‐13‐07); Computer Poker and Imperfect Information (WS‐13‐08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS‐13‐09); Intelligent Robotic Systems (WS‐13‐10); Intelligent Techniques for Web Personalization and Recommendation (WS‐13‐11); Learning Rich Representations from Low‐Level Sensors (WS‐13‐12); Plan, Activity, and Intent Recognition (WS‐13‐13); Space, Time, and Ambient Intelligence (WS‐13‐14); Trading Agent Design and Analysis (WS‐13‐15); and Statistical Relational Artificial Intelligence (WS‐13‐16).

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.997

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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