{"id":"W4288080293","doi":"10.1145/3385186","title":"OutdoorSent","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Sentiment analysis; Generalization; Class (philosophy); Artificial intelligence; Context (archaeology); Information retrieval; Machine learning; Data science; Geography","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":[0.0005943296,0.001512949,0.000601338,0.001437538,0.0005603801,0.001488761,0.001167124,0.0008974693,0.04375729],"category_scores_gemma":[0.00183611,0.0003556335,0.0008170568,0.001346834,0.0002801607,0.001590725,0.001668335,0.0007398073,0.03565536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005851979,"about_ca_system_score_gemma":0.000553981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009200886,"about_ca_topic_score_gemma":0.02793456,"domain_scores_codex":[0.9994859,0.00006084509,0.00003040826,0.0001925266,0.0001453101,0.00008506276],"domain_scores_gemma":[0.9994459,0.00007856687,0.00005764918,0.0002025786,0.0001474449,0.00006787295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006003662,0.0002013558,0.007123306,0.001031797,0.0001078897,0.0002567336,0.0002623461,0.001974337,0.007858192,0.002523339,0.8330821,0.1449781],"study_design_scores_gemma":[0.0002355426,0.0003763211,0.02375164,0.0002203265,0.00009147051,0.0008526375,0.0004851372,0.03933723,0.01567821,0.00531112,0.9135667,0.00009363806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.07698869,0.002548857,0.03119313,0.001366339,0.001797558,0.001065855,0.6478007,0.1061944,0.1310444],"genre_scores_gemma":[0.08711065,0.0006554211,0.03155265,0.0006010387,0.0002675717,0.0004741207,0.833451,0.003693275,0.04219421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04375729,"threshold_uncertainty_score":0.1463827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03513477824408741,"score_gpt":0.2490701315389516,"score_spread":0.2139353532948641,"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."}}