{"id":"W6958397227","doi":"10.6084/m9.figshare.13003097","title":"Supplementary Material.pdf","year":2020,"lang":"en","type":"other","venue":"Figshare","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":"Process (computing); Natural (archaeology); Identification (biology)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001419901,0.002038276,0.001312103,0.003794578,0.001480631,0.006800281,0.003229338,0.002243048,0.9502894],"category_scores_gemma":[0.01346008,0.001200714,0.001835802,0.004553515,0.000689727,0.005250835,0.004002562,0.001512921,0.8939342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246487,"about_ca_system_score_gemma":0.001437331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003885238,"about_ca_topic_score_gemma":0.005633673,"domain_scores_codex":[0.9991134,0.00009986568,0.00006478793,0.0002413372,0.0003434149,0.0001372234],"domain_scores_gemma":[0.9954417,0.001409822,0.0002214845,0.0008972244,0.001452337,0.0005774471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003172993,0.00001096405,0.00006629434,0.0001923777,0.000006574679,0.00001667981,0.00001192492,0.00005984707,0.00008382236,0.000796638,0.9846201,0.01410295],"study_design_scores_gemma":[0.00006919783,0.00001431194,0.0004968882,0.0001150684,0.00001093853,0.00005864807,0.00003030046,0.0003035262,0.0006096487,0.005872955,0.9923891,0.00002934131],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0004256813,0.0003241145,0.009954495,0.00133793,0.001699925,0.0002462549,0.6945066,0.06412889,0.2273761],"genre_scores_gemma":[0.00511201,0.0006498671,0.01461948,0.001576278,0.0007330651,0.0006401403,0.4743223,0.07404146,0.4283053],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04971063,"threshold_uncertainty_score":0.0709061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03190010625448755,"score_gpt":0.2594325945120813,"score_spread":0.2275324882575938,"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."}}