{"id":"W4398961580","doi":"10.7910/dvn/qi2t9a/9n4rv8","title":"20190201-icews-events.zip","year":2019,"lang":"es","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":[0.0007224102,0.0008595492,0.0007469057,0.0002838155,0.0003675593,0.0003827077,0.002299333,0.000456983,0.1777816],"category_scores_gemma":[0.00009279622,0.0008103593,0.00024049,0.0002345523,0.0001978293,0.00127586,0.0008789242,0.000803347,0.8889729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006240667,"about_ca_system_score_gemma":0.00008091948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005445445,"about_ca_topic_score_gemma":0.0002956562,"domain_scores_codex":[0.9953204,0.0002737402,0.000688997,0.001511261,0.001159841,0.001045746],"domain_scores_gemma":[0.9953684,0.0002168962,0.000479535,0.00343703,0.00001919035,0.0004789799],"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":[0.0001080637,0.0001541958,0.02149403,0.0004222752,0.0002170172,0.0001549591,0.00001407363,0.000312167,0.000004454665,0.000009129461,0.9741188,0.002990814],"study_design_scores_gemma":[0.000688292,0.0002793649,0.0473378,0.0002888744,0.0003682487,0.00001655651,0.0001707251,0.0001561297,0.000008372504,0.000009660006,0.9497501,0.0009259148],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001142216,0.00002333939,0.00002298422,0.00002292271,0.006697151,0.0007517167,0.9885042,0.00005304722,0.002782445],"genre_scores_gemma":[0.001272472,0.007403968,0.0004045856,0.0005693983,0.001074013,0.000008044251,0.9817278,0.00002858211,0.00751117],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7111914,"threshold_uncertainty_score":0.9994347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094821298722116,"score_gpt":0.2251435610974761,"score_spread":0.2041953481102549,"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."}}