{"id":"W4398799790","doi":"10.7910/dvn/qi2t9a/06tazg","title":"20181018-icews-events.zip","year":2018,"lang":"de","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":[0.00101023,0.0009531003,0.0007181441,0.0003109585,0.0006940341,0.0003900106,0.002575713,0.0005115759,0.3716405],"category_scores_gemma":[0.0001512898,0.00091449,0.000242026,0.0003165237,0.0004946924,0.001021302,0.001022857,0.0007475653,0.9512991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007812218,"about_ca_system_score_gemma":0.00009269007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005228028,"about_ca_topic_score_gemma":0.001199789,"domain_scores_codex":[0.9945834,0.0003386783,0.0008145763,0.001717945,0.001295003,0.001250409],"domain_scores_gemma":[0.9950134,0.0001699111,0.0005209252,0.003551373,0.0000314713,0.0007129053],"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":[0.0001439307,0.0001913592,0.006100913,0.0002292134,0.0003063342,0.0002604576,0.00002957879,0.00003760225,0.000002302176,0.000004772565,0.9912727,0.001420816],"study_design_scores_gemma":[0.0006951047,0.0004725333,0.009509685,0.000270291,0.000583497,0.00001786197,0.0001354338,0.0001273306,0.000009978186,0.00002572354,0.9871109,0.001041731],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006292936,0.00003299368,0.00003035971,0.00001944129,0.009208694,0.0006132807,0.9881095,0.00007985084,0.001276535],"genre_scores_gemma":[0.0003184626,0.006176768,0.0009141662,0.0006613287,0.003794214,0.00001094006,0.9836053,0.00003071556,0.004488051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5796586,"threshold_uncertainty_score":0.9993306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01693201941660247,"score_gpt":0.2184530807472577,"score_spread":0.2015210613306553,"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."}}