{"id":"W2798631888","doi":"10.1145/3209978.3210123","title":"Multi-level Abstraction Convolutional Model with Weak Supervision for Information Retrieval","year":2018,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada); Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Matching (statistics); Abstraction; Convolutional neural network; Semantic matching; Abstraction layer; Artificial intelligence; Training set; Word (group theory); Machine learning; Information retrieval; Natural language processing; Software; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007502997,0.0006160901,0.0007324535,0.0007052107,0.0002428764,0.0005611312,0.001266332,0.0008625194,0.001347989],"category_scores_gemma":[0.00202646,0.0003588493,0.000772143,0.000913009,0.0004423277,0.001839275,0.0007830047,0.001462863,0.00046749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275038,"about_ca_system_score_gemma":0.001053364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01494799,"about_ca_topic_score_gemma":0.01360426,"domain_scores_codex":[0.9997292,0.0000578612,0.0000205275,0.00007098875,0.00007143086,0.00004999415],"domain_scores_gemma":[0.9995215,0.000181276,0.00005884709,0.0001048757,0.0001041322,0.00002933718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004089264,0.0002443747,0.003440122,0.0002496801,0.0002223687,0.0001707839,0.0001710391,0.6073433,0.02209532,0.02560709,0.006414312,0.3336326],"study_design_scores_gemma":[0.000003701634,0.00002360231,0.0002377363,0.00000286575,0.00001594409,0.00001177267,0.000002290041,0.9952388,0.001024187,0.003147325,0.0002872593,0.000004537694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08892077,0.002382053,0.9023081,0.0006715421,0.00006504326,0.00008045969,0.0003017775,0.002721151,0.002549019],"genre_scores_gemma":[0.8813843,0.0009348533,0.1113223,0.0002541245,0.000059,0.0001133273,0.0006078622,0.0000738403,0.005250235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01494799,"threshold_uncertainty_score":0.02972198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06992547432501563,"score_gpt":0.2990072167605844,"score_spread":0.2290817424355688,"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."}}