{"id":"W4319719500","doi":"10.11606/d.55.2022.tde-09022023-114906","title":"ORTree: Aumentando a eficiência de buscas por similaridade diversificadas por meio de particionamento de dados","year":2022,"lang":"pt","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Adidas (Canada)","funders":"","keywords":"Humanities; Physics; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.003044988,0.001160639,0.0008790896,0.00178674,0.0009281328,0.002861858,0.001597357,0.001105052,0.01002763],"category_scores_gemma":[0.0167489,0.0006538433,0.0008698269,0.001866317,0.0007039828,0.003357908,0.002958214,0.001307833,0.002047434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038812,"about_ca_system_score_gemma":0.001420016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009548624,"about_ca_topic_score_gemma":0.009214291,"domain_scores_codex":[0.9977149,0.0005627541,0.000136983,0.0004413347,0.0009446501,0.000199484],"domain_scores_gemma":[0.9925019,0.003738804,0.0006059157,0.001442129,0.001287492,0.0004237794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0033228,0.0008474178,0.03414859,0.002145908,0.0005715725,0.0008479716,0.003378299,0.0563255,0.08829518,0.01496844,0.01648234,0.778666],"study_design_scores_gemma":[0.0005435228,0.004931005,0.09700243,0.0006500515,0.001288002,0.002472498,0.005571292,0.5987337,0.1027657,0.03413714,0.1513768,0.0005277709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.601842,0.005736772,0.3283472,0.002207061,0.0006870776,0.0009491839,0.002781258,0.01764181,0.03980757],"genre_scores_gemma":[0.8234659,0.00145255,0.1555986,0.0004153481,0.00008763098,0.0003858432,0.002540366,0.0006462609,0.0154076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01002763,"threshold_uncertainty_score":0.03354573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328816502073719,"score_gpt":0.2872527515110828,"score_spread":0.2639645864903457,"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."}}