{"id":"W3009019702","doi":"10.1007/978-3-030-42835-8_10","title":"Cross-Level Matching Model for Information Retrieval","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Matching (statistics); Information retrieval; Artificial intelligence; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002811847,0.0007255459,0.001945411,0.001338871,0.0004929011,0.001721025,0.003420152,0.002355201,0.01081486],"category_scores_gemma":[0.005193552,0.0005099664,0.001626126,0.002927494,0.0005437554,0.003814788,0.001441316,0.001922946,0.006226409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056985,"about_ca_system_score_gemma":0.0009344946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005826673,"about_ca_topic_score_gemma":0.004092312,"domain_scores_codex":[0.9986839,0.000529842,0.00009439453,0.0003128081,0.0002407614,0.0001383706],"domain_scores_gemma":[0.9983052,0.0009534868,0.00009733628,0.0003315823,0.0002634189,0.00004901826],"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":[0.000633857,0.0004317619,0.001921764,0.0006456257,0.0004710125,0.0002227315,0.000188938,0.2538562,0.007092749,0.1107132,0.0262998,0.5975224],"study_design_scores_gemma":[0.0000131884,0.00004804506,0.0003600863,0.00001141293,0.00006161341,0.00006664285,0.00001137539,0.9661072,0.0007731459,0.02959403,0.002938889,0.00001430857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008716634,0.00283315,0.983343,0.0003988051,0.0001601659,0.00004805295,0.0005382686,0.001399425,0.002562414],"genre_scores_gemma":[0.4443248,0.004640436,0.4849723,0.0007304963,0.0005921678,0.0004638933,0.005409931,0.000928888,0.05793713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01081486,"threshold_uncertainty_score":0.03617924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04501907099438952,"score_gpt":0.27833373218575,"score_spread":0.2333146611913605,"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."}}