{"id":"W4398958033","doi":"10.7910/dvn/qi2t9a/12rbfk","title":"20190428-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":"Zip code; Geography; Cartography","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":[0.001151396,0.0009717842,0.0008358052,0.0003163959,0.000425036,0.0003750347,0.002626612,0.0005261067,0.254825],"category_scores_gemma":[0.0001534935,0.0009378121,0.0002763822,0.0002908279,0.0002141851,0.001409431,0.001006663,0.0009709474,0.9227663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174198,"about_ca_system_score_gemma":0.0001229774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006130227,"about_ca_topic_score_gemma":0.0007387091,"domain_scores_codex":[0.9941162,0.0003201469,0.0007999449,0.001735964,0.00180648,0.001221305],"domain_scores_gemma":[0.9947134,0.0002330136,0.0005326107,0.003912224,0.0000337475,0.0005750681],"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":[0.0001329482,0.0001887906,0.008990899,0.0004149415,0.0002463659,0.0002145972,0.00003377476,0.0004478555,0.00000410948,0.000004637733,0.9870256,0.002295458],"study_design_scores_gemma":[0.0008881203,0.0003331783,0.01922304,0.0002955594,0.0004436643,0.00002192973,0.000319862,0.0002416958,0.000008481348,0.000008538749,0.9771308,0.00108515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008468768,0.00002902285,0.00002243798,0.00002235279,0.008546172,0.0008692549,0.9857767,0.0000556313,0.003831528],"genre_scores_gemma":[0.001376716,0.005666866,0.0004429049,0.0006684744,0.001158615,0.000008778963,0.9785804,0.00003200817,0.0120653],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6679413,"threshold_uncertainty_score":0.9993072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02230719661884454,"score_gpt":0.2239312664737648,"score_spread":0.2016240698549203,"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."}}