{"id":"W4398981446","doi":"10.7910/dvn/qi2t9a/dfbcnq","title":"20190302-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":"Event (particle physics); Physics","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":[0.001129732,0.0009732164,0.000835139,0.0003168374,0.0004299176,0.0003734276,0.002632423,0.0005265868,0.2689227],"category_scores_gemma":[0.0001528426,0.0009391248,0.000279018,0.0002892518,0.0002145467,0.001375534,0.001005559,0.0009716895,0.9231415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157937,"about_ca_system_score_gemma":0.0001208405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006332942,"about_ca_topic_score_gemma":0.0007231135,"domain_scores_codex":[0.9941229,0.0003188777,0.0008006396,0.001739552,0.001794483,0.001223543],"domain_scores_gemma":[0.9947025,0.000235763,0.000533369,0.003925958,0.00003309334,0.000569274],"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":[0.0001340952,0.0001854318,0.009325854,0.0004132314,0.0002469896,0.0002143428,0.00003249402,0.0004250919,0.000004103372,0.000004966216,0.986591,0.002422359],"study_design_scores_gemma":[0.0008944372,0.000331413,0.02025415,0.0002989345,0.000443059,0.00002159995,0.0003083289,0.0002356591,0.000008568539,0.00000894788,0.9761093,0.001085604],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000858938,0.00002870779,0.00002280657,0.00002195157,0.008562097,0.0008800233,0.9856065,0.00005567973,0.003963279],"genre_scores_gemma":[0.001496586,0.00580919,0.0004435254,0.0006785056,0.001148105,0.00000906518,0.9787027,0.00003208727,0.01168028],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6542189,"threshold_uncertainty_score":0.9993059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262475570320763,"score_gpt":0.2245063871368971,"score_spread":0.2018816314336895,"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."}}