{"id":"W2804405055","doi":"10.1007/978-3-319-92058-0_49","title":"Auto-detection of Safety Issues in Baby Products","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Random forest; Naive Bayes classifier; Computer science; Classifier (UML); Support vector machine; Logistic regression; Dimensionality reduction; Machine learning; Artificial intelligence; Product (mathematics); Commission; Data mining; Business; 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":[0.002074874,0.0003189085,0.0005535726,0.00121597,0.000117717,0.0003517202,0.002927404,0.0002035515,0.000008595196],"category_scores_gemma":[0.0002693326,0.0002910463,0.0001040724,0.003491631,0.0004005584,0.0004755715,0.002961756,0.0005621599,0.00001093511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002061792,"about_ca_system_score_gemma":0.0003900186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002349944,"about_ca_topic_score_gemma":0.0001298404,"domain_scores_codex":[0.9961919,0.0001552625,0.0007209072,0.001583432,0.0008475415,0.0005009795],"domain_scores_gemma":[0.9975193,0.0001713631,0.0004002457,0.001503247,0.0003383798,0.00006747168],"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":[0.00001843547,0.0002018001,0.005306405,0.000179854,0.00002574832,0.00001762778,0.006039568,0.1926076,0.01519397,0.000311331,0.00001963467,0.7800781],"study_design_scores_gemma":[0.0001710254,0.00008805334,0.01134656,0.0003407057,0.000004194118,0.000004409818,6.816256e-7,0.882554,0.09833238,0.006698393,0.0001480686,0.0003115063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04877974,0.000500172,0.9463439,0.001218867,0.002780315,0.0002786997,0.000001184655,0.00007445669,0.00002265664],"genre_scores_gemma":[0.7290605,0.00004056224,0.2704359,0.0001129589,0.0003314597,0.000006471686,0.000002597847,0.000007276851,0.000002284786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7797666,"threshold_uncertainty_score":0.9999542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921199339360233,"score_gpt":0.2825709766098138,"score_spread":0.2633589832162115,"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."}}