{"id":"W4398957076","doi":"10.7910/dvn/qi2t9a/zquobp","title":"20190227-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":[0.0007125715,0.000856578,0.0007278292,0.0002906965,0.000378008,0.0003890134,0.002294569,0.0004421804,0.1751577],"category_scores_gemma":[0.00009215269,0.0008135641,0.000237655,0.0002472346,0.000193535,0.001256546,0.0008917534,0.0008107118,0.8721834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006531879,"about_ca_system_score_gemma":0.00008213579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005381628,"about_ca_topic_score_gemma":0.000308827,"domain_scores_codex":[0.9952981,0.0002741298,0.000685396,0.001515146,0.001183762,0.001043483],"domain_scores_gemma":[0.9954151,0.0002173895,0.000468703,0.003414334,0.00001866686,0.0004658368],"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":[0.0001084732,0.0001590445,0.01847745,0.0005092467,0.0001679491,0.0001684901,0.00001462894,0.0003588868,0.000005124005,0.000009447414,0.977149,0.002872303],"study_design_scores_gemma":[0.0006873351,0.0002832999,0.04531045,0.0002795674,0.0003760027,0.00001714018,0.0001837142,0.0001091083,0.00001021261,0.000009608473,0.9518065,0.0009270249],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001526593,0.00002262036,0.00002059463,0.00002159338,0.00662442,0.0007446326,0.9881468,0.0000533885,0.002839318],"genre_scores_gemma":[0.001396624,0.007264187,0.0003737773,0.0005579711,0.001032153,0.000008271415,0.9819835,0.00002844896,0.007355105],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6970257,"threshold_uncertainty_score":0.9994316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02090748484661048,"score_gpt":0.2253936049460931,"score_spread":0.2044861200994827,"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."}}