{"id":"W4399007959","doi":"10.7910/dvn/qi2t9a/mopazp","title":"20190217-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":"Environmental 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001011153,0.001927776,0.001202481,0.003559409,0.0007823671,0.002961483,0.00231412,0.001472777,0.2580388],"category_scores_gemma":[0.005159178,0.00100045,0.0008594159,0.007460905,0.0004254208,0.002172641,0.00213867,0.001294064,0.2563319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646866,"about_ca_system_score_gemma":0.001666217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02325304,"about_ca_topic_score_gemma":0.0277181,"domain_scores_codex":[0.9992841,0.00007016291,0.00008851349,0.0002178224,0.0001491566,0.0001903423],"domain_scores_gemma":[0.9974406,0.0006014762,0.0002472251,0.0007268283,0.00056201,0.0004218982],"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":[0.00004274066,0.000008151364,0.0002969742,0.000156233,0.000007916026,0.000006221449,0.00001147326,0.00009582537,0.00004641477,0.0004079725,0.9979404,0.0009797761],"study_design_scores_gemma":[0.0002369216,0.00000973826,0.002291432,0.00009910042,0.00001068879,0.0000138225,0.00004234729,0.0003662615,0.0003271688,0.0009833963,0.9955988,0.0000202196],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004673388,0.000008261277,0.00003314785,0.0000332973,0.00001317507,0.000006189889,0.9982702,0.0005283764,0.001060663],"genre_scores_gemma":[0.000311603,0.00001963909,0.0001203107,0.00003976801,0.000009160547,0.00003788702,0.9982415,0.0003220891,0.0008980635],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7419612,"threshold_uncertainty_score":0.8632256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163347592776325,"score_gpt":0.2265062717386954,"score_spread":0.2048727958109322,"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."}}