{"id":"W4387394661","doi":"10.1609/aiide.v19i1.27520","title":"Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Tree (set theory); Artificial intelligence; Machine learning; Quality (philosophy); Theoretical computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001270037,0.0008620769,0.0007261086,0.001571593,0.0006287451,0.001364784,0.002438796,0.001174975,0.0051307],"category_scores_gemma":[0.005521629,0.0006620258,0.001228774,0.001113657,0.001194866,0.002283851,0.002456009,0.001728134,0.001506273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001774,"about_ca_system_score_gemma":0.001056202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002736057,"about_ca_topic_score_gemma":0.00493138,"domain_scores_codex":[0.9986066,0.0003429609,0.0000696677,0.0002870913,0.0005694542,0.0001242465],"domain_scores_gemma":[0.997304,0.001336713,0.0001641091,0.000654269,0.0004268935,0.0001140374],"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":[0.0002634508,0.0002696098,0.002907229,0.0002831359,0.00007903259,0.0003471966,0.001198967,0.2849483,0.03155989,0.09846014,0.009309934,0.5703732],"study_design_scores_gemma":[0.00002963733,0.00007662183,0.000315608,0.00002116873,0.00002244045,0.0001260288,0.00009775681,0.9482113,0.006536467,0.03715867,0.007384676,0.00001959493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0048084,0.00004666992,0.9926851,0.0000678963,0.0000110616,0.00006672572,0.00008125597,0.0007985265,0.00143436],"genre_scores_gemma":[0.1372304,0.00007882158,0.857893,0.0001105573,0.00002226099,0.0002281674,0.0007804372,0.0005814199,0.003074936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0051307,"threshold_uncertainty_score":0.01716393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2060435326138589,"score_gpt":0.3205882956809046,"score_spread":0.1145447630670457,"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."}}