{"id":"W6958123803","doi":"10.6068/dp16da67aeb8437","title":"TREND: United Nations Economic Commission for Europe. Gender Statistics [Archive]: Victims of Crime | Country: Armenia | Selection 1: Both sexes | Selection 2: Robbery | Selection 3: Victims of crime, 1980 - 2003. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 054-003-054","year":2019,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commission; Official statistics; Economic statistics; Selection (genetic algorithm); Crime statistics; Publishing; Economic data; Economic Justice","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001621825,0.001764113,0.001704,0.006706246,0.0008778789,0.002986649,0.00228819,0.0009095087,0.08905907],"category_scores_gemma":[0.01258045,0.001157469,0.0009719066,0.02322419,0.0003218392,0.002804424,0.001628142,0.002350188,0.09052851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00245782,"about_ca_system_score_gemma":0.006138014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1024922,"about_ca_topic_score_gemma":0.06513954,"domain_scores_codex":[0.9980945,0.0002533028,0.000329951,0.0003890041,0.0005950571,0.0003381746],"domain_scores_gemma":[0.9933175,0.0008499247,0.0009771185,0.0006466256,0.00385441,0.0003543154],"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":[0.00002371889,0.000007355658,0.0006128774,0.0002325732,0.00001126078,0.000008411873,0.00001782708,0.00004681584,0.00001376652,0.0002636728,0.996568,0.0021938],"study_design_scores_gemma":[0.00008086982,0.00001653203,0.01555284,0.0007489974,0.00003446936,0.0000256254,0.0002500784,0.0001108234,0.00009506546,0.0005025688,0.9825582,0.0000239893],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001214124,0.00008063852,0.00004566943,0.00009539028,0.00007897264,0.0000289368,0.9980661,0.00006089835,0.001421927],"genre_scores_gemma":[0.0007475243,0.0002600802,0.0002501695,0.00008058386,0.00003437251,0.0002953665,0.9949641,0.00009433831,0.003273495],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1024922,"threshold_uncertainty_score":0.2979323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03566392572501376,"score_gpt":0.2888282484591763,"score_spread":0.2531643227341626,"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."}}