{"id":"W1536212463","doi":"10.1007/978-3-540-77092-3_43","title":"Extraction and Classification of User Behavior","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Markov chain; Generator (circuit theory); Artificial neural network; User group; Artificial intelligence; Human–computer interaction; Machine learning; Multimedia; Power (physics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0007326177,0.000231727,0.0002369011,0.0007196671,0.0000910018,0.0002586304,0.001327483,0.0001602552,0.00000974242],"category_scores_gemma":[0.00002238158,0.0002181096,0.00004392163,0.0003527831,0.0003879037,0.0009612539,0.0006835288,0.0003133265,0.00001222963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007714709,"about_ca_system_score_gemma":0.00007908987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001037846,"about_ca_topic_score_gemma":0.00002617921,"domain_scores_codex":[0.9979453,0.00001105763,0.0003527136,0.0008237885,0.0005999669,0.0002671537],"domain_scores_gemma":[0.9985726,0.0001462466,0.0002671867,0.0008142735,0.0001272374,0.00007246732],"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":[0.000001509673,0.00002157593,0.000121735,0.00001823549,0.000002989101,0.00001801332,0.00008035819,0.00006545225,0.0003931249,0.03325179,0.00001320233,0.966012],"study_design_scores_gemma":[0.001063046,0.0006178496,0.08165832,0.0008061373,0.00009257,0.0001741185,0.000001109825,0.7635642,0.006564717,0.1136594,0.02953398,0.002264612],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002444553,0.0001640683,0.9951594,0.0001461642,0.0008533914,0.0002524723,0.000003597408,0.0000577551,0.003118735],"genre_scores_gemma":[0.1090427,0.0001191314,0.8890696,0.0003349629,0.0003097344,0.000009156119,0.00001646476,0.0000246738,0.001073587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9637474,"threshold_uncertainty_score":0.8894253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04153065881389877,"score_gpt":0.2941857990500126,"score_spread":0.2526551402361139,"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."}}