{"id":"W4398923162","doi":"10.7910/dvn/qi2t9a/27ll2c","title":"20190430-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","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001026951,0.001814207,0.001117395,0.003890966,0.0008007384,0.00303569,0.00236128,0.001406774,0.2553515],"category_scores_gemma":[0.005554108,0.001086085,0.0008648694,0.00766599,0.0004209508,0.002238189,0.002115682,0.001309232,0.2598474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001621732,"about_ca_system_score_gemma":0.001648061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02019368,"about_ca_topic_score_gemma":0.02489482,"domain_scores_codex":[0.9992674,0.00007089103,0.00009613397,0.0002258777,0.0001497251,0.0001899903],"domain_scores_gemma":[0.9970834,0.0006736589,0.0002871475,0.0008741937,0.0005929272,0.0004887786],"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":[0.00004600546,0.000009268642,0.0003357198,0.0001653524,0.000008182942,0.000006847642,0.00001260584,0.00009253527,0.00005907962,0.0004390012,0.9978045,0.001020956],"study_design_scores_gemma":[0.0002317555,0.00001049943,0.002506423,0.0000942665,0.00001078983,0.00001589822,0.00004220903,0.0003445277,0.0003736232,0.0009412292,0.995408,0.00002069524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005404474,0.000009050354,0.00003673618,0.00003359416,0.00001401803,0.00000652413,0.9980304,0.0006872837,0.001128379],"genre_scores_gemma":[0.0003407444,0.00002068157,0.0001332805,0.00004218228,0.000009753847,0.00003715748,0.9980791,0.0004339786,0.0009032181],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7446485,"threshold_uncertainty_score":0.8542358,"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."}}