{"id":"W4398900022","doi":"10.7910/dvn/qi2t9a/zzujwm","title":"20190129-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":"Computer 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001137623,0.0009709456,0.0008310844,0.0003150199,0.0004302102,0.0003715187,0.002606371,0.0005257598,0.258545],"category_scores_gemma":[0.0001510564,0.0009370518,0.0002781027,0.000288424,0.0002129204,0.001433463,0.001006945,0.0009700956,0.9236568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001148861,"about_ca_system_score_gemma":0.000117927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005887886,"about_ca_topic_score_gemma":0.0005291632,"domain_scores_codex":[0.9941424,0.0003184461,0.0007972954,0.001722363,0.001783367,0.001236102],"domain_scores_gemma":[0.9947319,0.0002355704,0.0005315283,0.003895894,0.00003272541,0.0005723874],"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":[0.0001331355,0.0001893549,0.008865737,0.0004208497,0.0002420426,0.000207649,0.00003373117,0.0003742078,0.000006616458,0.000004727313,0.9873882,0.002133775],"study_design_scores_gemma":[0.0008741821,0.0003268982,0.02747898,0.0002912417,0.000443577,0.0000216787,0.0003162332,0.0002134435,0.00000863785,0.000008081833,0.9689416,0.001075454],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00103695,0.00002915057,0.00002155917,0.00002195774,0.008436344,0.0008675641,0.9858212,0.00005521667,0.00371003],"genre_scores_gemma":[0.001447702,0.005922429,0.0004567369,0.0006669385,0.001166822,0.000008869612,0.9786869,0.00003208296,0.0116115],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6651118,"threshold_uncertainty_score":0.999308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235813900535796,"score_gpt":0.2250519119911147,"score_spread":0.2026937729857568,"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."}}