{"id":"W4398863530","doi":"10.7910/dvn/qi2t9a/loxyt4","title":"20190203-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":[0.001131575,0.0009718539,0.0008394293,0.0003082932,0.000428572,0.0003692751,0.002630801,0.0005247094,0.2584811],"category_scores_gemma":[0.000156195,0.0009379589,0.0002792381,0.0002894617,0.0002158462,0.001406659,0.001005579,0.0009739401,0.9231112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145374,"about_ca_system_score_gemma":0.0001203712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006011065,"about_ca_topic_score_gemma":0.0007001768,"domain_scores_codex":[0.9941331,0.000322452,0.0008032132,0.00174313,0.001777552,0.001220607],"domain_scores_gemma":[0.9947191,0.0002394261,0.0005361989,0.003896677,0.00003246312,0.0005761546],"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":[0.0001331799,0.0001865581,0.009110394,0.000419316,0.0002466786,0.0002179854,0.00003348778,0.0004297337,0.000004280328,0.000004732412,0.986849,0.002364627],"study_design_scores_gemma":[0.0008796173,0.0003329872,0.01975846,0.0002947711,0.0004424161,0.00002190477,0.0003127477,0.0002384755,0.000008827881,0.000008525965,0.9766173,0.001083916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008273114,0.00002897957,0.00002326211,0.00002372889,0.008452579,0.0008727022,0.9860657,0.00005577601,0.00364993],"genre_scores_gemma":[0.001426957,0.005926522,0.0004471229,0.0006968998,0.001174213,0.000008763275,0.9789047,0.00003206231,0.01138276],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6646302,"threshold_uncertainty_score":0.9993071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264317154450872,"score_gpt":0.2242695930882851,"score_spread":0.2016264215437764,"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."}}