{"id":"W117603095","doi":"","title":"Context-driven predictions","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Observable; Computer science; Partially observable Markov decision process; Context (archaeology); Markov model; Markov process; Markov decision process; Hidden Markov model; Simple (philosophy); Markov chain; Abstraction; Hidden semi-Markov model; Context model; Maximum-entropy Markov model; Artificial intelligence; Theoretical computer science; Variable-order Markov model; Machine learning; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006259864,0.0002512492,0.0002069327,0.0003530605,0.0001926458,0.0004296419,0.001469811,0.000127992,0.0003692459],"category_scores_gemma":[0.0002217687,0.0002458607,0.0001314191,0.0003448965,0.0001635362,0.0005142933,0.0002209654,0.0004229533,0.001368692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001325473,"about_ca_system_score_gemma":0.0001557756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009251789,"about_ca_topic_score_gemma":0.0001605791,"domain_scores_codex":[0.9974033,0.00004972886,0.0007352735,0.0006532522,0.0007019011,0.0004565528],"domain_scores_gemma":[0.9982211,0.0001668848,0.0001925569,0.00053533,0.0006451613,0.000238945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002125263,0.0001275344,0.00002899735,0.0000015314,0.00001991948,0.00002637391,0.0003375464,0.0007174929,0.00271927,0.7793719,0.00019786,0.2164303],"study_design_scores_gemma":[0.00005965821,0.0002571557,0.0005771582,0.000137454,0.000006925593,0.00004620488,0.000456522,0.6516585,0.05794367,0.2861259,0.002257324,0.0004734524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003543456,0.00001186526,0.9496447,0.005731472,0.002213522,0.0001580532,0.00001219988,0.0003592764,0.0383255],"genre_scores_gemma":[0.984145,0.00003321581,0.01387516,0.001186845,0.0002922821,0.00001801656,0.000009081795,0.0000127032,0.0004276786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9806015,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1336036417609611,"score_gpt":0.3360765873948148,"score_spread":0.2024729456338537,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}