{"id":"W2952162622","doi":"10.13140/rg.2.2.11166.28489","title":"Random Tessellation Forests","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mondrian; Computer science; Process (computing); Binary tree; Tree (set theory); Monte Carlo method; Space partitioning; Tessellation (computer graphics); Domain (mathematical analysis); Binary number; Point process; Algorithm; Inference; Theoretical computer science; Mathematics; Artificial intelligence; Computer graphics (images); Statistics","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":[0.002020047,0.0006834909,0.001419018,0.002129844,0.000876582,0.001639649,0.001826371,0.001340287,0.004095164],"category_scores_gemma":[0.008106691,0.0006033056,0.001973366,0.001900385,0.000922867,0.001780411,0.001399077,0.001130826,0.001519282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009107205,"about_ca_system_score_gemma":0.0007233876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004967187,"about_ca_topic_score_gemma":0.007645099,"domain_scores_codex":[0.9982527,0.0006179325,0.0001020589,0.0004522969,0.0003725806,0.0002023341],"domain_scores_gemma":[0.9961671,0.002207425,0.0002599736,0.0006503008,0.000580263,0.0001349738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002036418,0.00006976713,0.004670214,0.0002338371,0.0002205894,0.0002349115,0.0002048959,0.6426662,0.00285464,0.1388148,0.01302208,0.1968044],"study_design_scores_gemma":[0.00001770144,0.00001411386,0.0003886984,0.0000247939,0.00001516089,0.0001095049,0.00002062906,0.9314904,0.0009111418,0.06292739,0.004065923,0.00001446566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01506932,0.0007766537,0.9799299,0.0001509982,0.00008708563,0.0001018222,0.0005493588,0.000988825,0.002346057],"genre_scores_gemma":[0.4055489,0.0008910597,0.5815834,0.000451689,0.0002348052,0.0004930595,0.00410524,0.0004507679,0.006241011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004967187,"threshold_uncertainty_score":0.01369971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05804052649545609,"score_gpt":0.1790589830140752,"score_spread":0.1210184565186191,"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."}}