{"id":"W4398970419","doi":"10.7910/dvn/qi2t9a/ihsgf4","title":"20181120-icews-events.zip","year":2018,"lang":"eu","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","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":[0.0009119977,0.0008929324,0.0006756032,0.0002853692,0.0006404053,0.0003615843,0.002447706,0.0004757895,0.2811283],"category_scores_gemma":[0.0001354154,0.000849844,0.0002260595,0.0002927817,0.0004399585,0.001196936,0.0009753284,0.0006977801,0.8389544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007151459,"about_ca_system_score_gemma":0.00008263227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008479253,"about_ca_topic_score_gemma":0.001543665,"domain_scores_codex":[0.9949464,0.0003018045,0.000752256,0.001619774,0.001212703,0.001167023],"domain_scores_gemma":[0.9952887,0.0001651925,0.0004839952,0.00338777,0.00002854454,0.0006458205],"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":[0.0001186077,0.0001762521,0.006419532,0.0002195161,0.0002159102,0.0002221287,0.00002582486,0.00003131913,0.00000260672,0.000006427706,0.9904905,0.002071378],"study_design_scores_gemma":[0.0006534831,0.0004337858,0.01891133,0.0002524277,0.000412057,0.00002462731,0.0001424021,0.0001018678,0.00001281853,0.0000283451,0.978062,0.0009648714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008537623,0.00001658926,0.00002213967,0.0000187488,0.007254893,0.0005744377,0.9874242,0.00007715987,0.003758101],"genre_scores_gemma":[0.0005800218,0.003848564,0.0008575869,0.0006474544,0.002583991,0.00001013124,0.9846405,0.00002923147,0.006802534],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5578262,"threshold_uncertainty_score":0.9993953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778621367326625,"score_gpt":0.2203298193493805,"score_spread":0.2025436056761143,"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."}}