{"id":"W7134840113","doi":"10.2905/jrc.n7w2v3r","title":"2023 PREDICT Dataset","year":2023,"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; Index (typography); Information and Communications Technology; Gross value added; Value (mathematics); Per capita; Revenue; Earnings; Economic indicator","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":[0.001147553,0.002779933,0.001420358,0.002872423,0.001004058,0.002173192,0.004029579,0.003037301,0.06336811],"category_scores_gemma":[0.004745857,0.0006815504,0.002037632,0.003969586,0.0004527436,0.002239002,0.001703369,0.002454355,0.09550533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449969,"about_ca_system_score_gemma":0.002070481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02331532,"about_ca_topic_score_gemma":0.04090615,"domain_scores_codex":[0.9988943,0.0002072878,0.0001158932,0.0003734262,0.000243624,0.0001655404],"domain_scores_gemma":[0.9984517,0.0004313132,0.0001136965,0.000384231,0.0004906975,0.0001282776],"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":[0.0001126921,0.00006884874,0.001852502,0.0004210333,0.00005613266,0.00006362799,0.00001414174,0.001740693,0.0001793566,0.0005928966,0.9890554,0.005842704],"study_design_scores_gemma":[0.0004983937,0.0001329913,0.00746684,0.0002794291,0.00008161763,0.0002166057,0.0001366371,0.01281523,0.000966496,0.003854724,0.9734687,0.00008220035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006468752,0.0001392212,0.0002789714,0.0002151467,0.00009958252,0.0000313678,0.9961437,0.001297725,0.001147305],"genre_scores_gemma":[0.0006744791,0.00004737459,0.0004807556,0.0000903239,0.00001409437,0.00005952041,0.9979913,0.00006013192,0.0005819848],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06336811,"threshold_uncertainty_score":0.2119874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048617585708311,"score_gpt":0.3862439091540438,"score_spread":0.2813821505832128,"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."}}