{"id":"W4398898149","doi":"10.7910/dvn/qi2t9a/pzqjkj","title":"20181006-icews-events.zip","year":2018,"lang":"ru","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":"Environmental science; 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":[0.00100452,0.000968129,0.0007298287,0.0003128836,0.0007182424,0.0003952963,0.002657494,0.0005188988,0.3018894],"category_scores_gemma":[0.0001461094,0.000930428,0.0002461775,0.000321943,0.000499926,0.001306166,0.0010533,0.0007537957,0.8530655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008032973,"about_ca_system_score_gemma":0.0000931347,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008819639,"about_ca_topic_score_gemma":0.001805046,"domain_scores_codex":[0.9945275,0.000326948,0.0008254804,0.001747059,0.00130006,0.001272986],"domain_scores_gemma":[0.9949548,0.0001798157,0.0005315773,0.003597363,0.00003125265,0.0007051497],"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":[0.0001376252,0.0001932666,0.007953675,0.0002433232,0.0002345922,0.0002394744,0.00002947313,0.00003841172,0.000002901897,0.000005004385,0.9886633,0.002258925],"study_design_scores_gemma":[0.0007036789,0.0004735264,0.02226889,0.0002844058,0.0004497611,0.00002643988,0.0001790682,0.0001161977,0.00001474712,0.00002513751,0.9744135,0.00104467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001105796,0.00001723289,0.00002680584,0.00002032069,0.007753375,0.0006278628,0.9877189,0.00008014071,0.002649518],"genre_scores_gemma":[0.0007183299,0.004327564,0.0008276503,0.0006470548,0.002793297,0.00001074159,0.9849274,0.00003092633,0.00571707],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5511761,"threshold_uncertainty_score":0.9993146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749912572322328,"score_gpt":0.2208294926697861,"score_spread":0.2033303669465628,"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."}}