{"id":"W1710084411","doi":"10.1007/978-3-540-75256-1_62","title":"A Qualitative Hidden Markov Model for Spatio-temporal Reasoning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Hidden Markov model; Construct (python library); Artificial intelligence; Representation (politics); Markov process; Markov model; Markov chain; Maximum-entropy Markov model; Machine learning; Variable-order Markov model; Hidden semi-Markov model; Theoretical computer science; Programming language; 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"],"consensus_categories":[],"category_scores_codex":[0.002712646,0.0005437692,0.0005240192,0.0009682384,0.0003039467,0.0007997855,0.003848118,0.0002404129,0.00001054655],"category_scores_gemma":[0.000141917,0.0005161415,0.000168356,0.0005473351,0.0004518536,0.001320528,0.001701066,0.0005070831,0.00002276811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002617477,"about_ca_system_score_gemma":0.0003891105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002970695,"about_ca_topic_score_gemma":0.0001822617,"domain_scores_codex":[0.9958098,0.00003375327,0.0005875428,0.001709097,0.001042318,0.0008174942],"domain_scores_gemma":[0.9972292,0.0006111593,0.0003888438,0.001304681,0.0002936617,0.0001724278],"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":[0.00001469397,0.00002243482,0.000007242017,0.00005333239,0.00001861643,0.00003461295,0.004431037,0.005029488,0.000002820379,0.07782879,0.0002590831,0.9122978],"study_design_scores_gemma":[0.0002720421,0.00009636605,0.000004757312,0.0002055765,0.000008259189,0.000006082916,0.000001124321,0.821552,0.0000493367,0.1759689,0.001297142,0.0005384685],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001073278,0.000166354,0.9928856,0.0005625582,0.001042526,0.0007212895,0.00003562307,0.0001836345,0.004391612],"genre_scores_gemma":[0.00563715,0.00001478282,0.9902669,0.00115424,0.0004037126,0.00002197887,0.00007119529,0.00003794664,0.00239213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9117594,"threshold_uncertainty_score":0.999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269835117297612,"score_gpt":0.3273504401116056,"score_spread":0.2746520889386295,"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."}}