{"id":"W2948595979","doi":"","title":"OutdoorSent","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","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":[0.0006198147,0.001507824,0.0005955209,0.001569634,0.0005847486,0.001553277,0.001209139,0.0009219957,0.04840586],"category_scores_gemma":[0.002004626,0.0003715481,0.0008201083,0.001483946,0.000284509,0.001679644,0.001795299,0.0007616444,0.04038443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000601124,"about_ca_system_score_gemma":0.0005701472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009308935,"about_ca_topic_score_gemma":0.02753963,"domain_scores_codex":[0.9994525,0.0000648101,0.00003258285,0.0002015091,0.0001605333,0.00008817141],"domain_scores_gemma":[0.9993553,0.00009284274,0.00006710831,0.0002415891,0.00016433,0.00007896733],"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":[0.0005083358,0.0001804264,0.006636968,0.0009262799,0.00009240662,0.000228742,0.0002426338,0.00170783,0.006394158,0.002465638,0.8554339,0.1251827],"study_design_scores_gemma":[0.0002114945,0.0003061292,0.02137672,0.000205198,0.00007553247,0.0007526301,0.0004257573,0.03209881,0.01321659,0.00521303,0.9260296,0.00008854125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06175021,0.002259046,0.02745013,0.001314249,0.001611113,0.0009087426,0.6715437,0.1062232,0.1269397],"genre_scores_gemma":[0.07551117,0.0006022492,0.0273165,0.0005530725,0.0002543227,0.000441508,0.8506204,0.003825848,0.04087481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04840586,"threshold_uncertainty_score":0.1619337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1134802143256853,"score_gpt":0.1753705201339679,"score_spread":0.06189030580828263,"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."}}