{"id":"W7134845808","doi":"10.2905/jrc.s8qbadg","title":"2020 PREDICT Dataset (deprecated)","year":2020,"lang":"","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Competitor analysis; Nowcasting; Productivity; Government (linguistics); Index (typography); Information and Communications Technology; Revenue; Earnings; Value (mathematics); Variety (cybernetics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001531268,0.002509459,0.001480706,0.003204434,0.0008854906,0.002382527,0.00349013,0.002724258,0.04094659],"category_scores_gemma":[0.004956109,0.0006639137,0.002042535,0.004351134,0.0004793114,0.002138608,0.002116653,0.002003696,0.08212898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401672,"about_ca_system_score_gemma":0.001950828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03004624,"about_ca_topic_score_gemma":0.04873599,"domain_scores_codex":[0.9986619,0.0002401851,0.000137886,0.0003734938,0.0003361498,0.0002504055],"domain_scores_gemma":[0.998139,0.0003297515,0.0001687515,0.0004595631,0.0007048227,0.0001980292],"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":[0.00008506054,0.0000406319,0.001227592,0.0003242665,0.00003818332,0.00003219838,0.00001195181,0.0008213689,0.0001291138,0.0004362164,0.9933856,0.003467846],"study_design_scores_gemma":[0.0003737245,0.00008452302,0.007439491,0.0002989864,0.00005684112,0.0001541052,0.0001117824,0.005072386,0.0005464705,0.001828081,0.9839662,0.00006730304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000415737,0.0001201773,0.0001555628,0.0001384655,0.00008787984,0.00001916069,0.9975592,0.0006668707,0.0008369499],"genre_scores_gemma":[0.0004656674,0.00004036718,0.0002744136,0.00006802286,0.00001460738,0.00003816563,0.9986517,0.00004355772,0.0004035625],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04094659,"threshold_uncertainty_score":0.1369801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06696298998859566,"score_gpt":0.3464305125410634,"score_spread":0.2794675225524677,"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."}}