{"id":"W2080872143","doi":"10.3166/ria.22.589-608","title":"Reasoning about Models of Context. A Context-Oriented Logical Language for Knowledge-Based Context-Aware Applications","year":2008,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Cognitive science; Linguistics; Natural language processing; Psychology; History; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006705704,0.0003763404,0.0007353451,0.0002778426,0.0004490222,0.00007809401,0.001120816,0.0002153793,0.00006972326],"category_scores_gemma":[0.0003259491,0.0003821841,0.0004124486,0.0009252015,0.0003518988,0.000553541,0.0001886379,0.0002853128,0.0002199437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001344244,"about_ca_system_score_gemma":0.0003088918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001827796,"about_ca_topic_score_gemma":0.0001916496,"domain_scores_codex":[0.9968206,0.0002070204,0.001096392,0.000960868,0.0003261542,0.0005889914],"domain_scores_gemma":[0.9954035,0.001572115,0.0005282165,0.001200775,0.001046959,0.000248399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002123029,0.001837282,0.0006240786,0.0004823908,0.0001515656,0.00004435271,0.02213434,0.007564676,0.005588107,0.1608307,0.001791519,0.7987387],"study_design_scores_gemma":[0.0004622177,0.0002703235,0.00003874921,0.0004128964,0.00002522432,0.00009101289,0.004801639,0.9171755,0.05587164,0.0009090157,0.01941805,0.0005237701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01254111,0.002657357,0.9803776,0.0002961372,0.0003014192,0.001832848,0.00009721334,0.0003297571,0.001566602],"genre_scores_gemma":[0.9928581,0.0000367935,0.004485574,0.0003045414,0.0001277969,0.0009380311,0.00004353428,0.00003804557,0.001167624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9803169,"threshold_uncertainty_score":0.999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07886819843786519,"score_gpt":0.3041189675546598,"score_spread":0.2252507691167946,"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."}}