{"id":"W4398981610","doi":"10.7910/dvn/qi2t9a/qwzhbg","title":"20190205-icews-events.zip","year":2019,"lang":"ja","type":"dataset","venue":"Harvard Dataverse","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Environmental science; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00110657,0.0009708458,0.0008370583,0.000306415,0.0004259235,0.0003516144,0.002598836,0.0005224989,0.2561682],"category_scores_gemma":[0.000155511,0.0009370297,0.0002788131,0.000287537,0.0002116079,0.001394218,0.001000562,0.000971935,0.9232149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142912,"about_ca_system_score_gemma":0.0001175974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0060527,"about_ca_topic_score_gemma":0.0007085943,"domain_scores_codex":[0.994163,0.0003211481,0.0008010187,0.001738388,0.001761485,0.00121501],"domain_scores_gemma":[0.9947432,0.0002384517,0.0005347463,0.003876849,0.00003225419,0.0005745455],"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":[0.0001327894,0.0001863433,0.008674401,0.0004168348,0.0002464854,0.0002176913,0.00003271838,0.0004281242,0.00000301917,0.000004519044,0.9870415,0.002615619],"study_design_scores_gemma":[0.0008771219,0.00033281,0.01912245,0.000292548,0.0004422922,0.00002191892,0.0003088042,0.0002382157,0.000006321845,0.000008481225,0.9772652,0.001083841],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007066035,0.00002772231,0.00002248946,0.00002343905,0.00837102,0.0008694662,0.9846271,0.0000556675,0.005296548],"genre_scores_gemma":[0.0009680591,0.007554234,0.0004445023,0.0006930346,0.001160019,0.000008722674,0.9747737,0.00003188679,0.01436582],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6670467,"threshold_uncertainty_score":0.999308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223039064184811,"score_gpt":0.2237440348099678,"score_spread":0.2014401283914867,"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."}}