{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002318546,0.0002223402,0.0002402148,0.0002332439,0.0000823618,0.0002481457,0.00204001,0.0001486236,0.00003186001],"category_scores_gemma":[0.00001589411,0.0002476233,0.000144488,0.0003919258,0.00003485488,0.0007501804,0.002740223,0.0002770963,0.0005145002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007628104,"about_ca_system_score_gemma":0.00006782481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004955462,"about_ca_topic_score_gemma":0.00002236744,"domain_scores_codex":[0.9985521,0.0000679733,0.0001363016,0.0008939254,0.00009375387,0.0002558904],"domain_scores_gemma":[0.9982058,0.0000758945,0.0001856653,0.001380723,0.00007589458,0.00007602201],"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":[0.00004884087,0.0001099629,0.0144487,0.0001919104,0.0001796596,0.000268004,0.0001369351,0.5927991,0.000007083804,0.3776718,0.007600537,0.006537434],"study_design_scores_gemma":[0.0009578783,0.00002051761,0.003205466,0.00004976838,0.00004136554,7.189498e-7,0.000009517767,0.9533905,0.00002219157,0.03783376,0.004131969,0.0003363229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02792211,0.00003546619,0.9587521,0.00009646794,0.001088322,0.000383982,0.00001226521,0.0002539156,0.01145536],"genre_scores_gemma":[0.9897358,0.0001014724,0.002171761,0.00006220368,0.00008830776,5.151493e-7,0.00007557465,0.00001149921,0.007752827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9618137,"threshold_uncertainty_score":0.9999976,"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."}}