{"id":"W3174688402","doi":"10.1007/978-3-030-79987-8_23","title":"Complexity and Algorithms for MUL-Tree Pruning","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Pruning; Tree (set theory); Parameterized complexity; Set (abstract data type); Heuristic; Computer science; Algorithm; Search tree; Tree rearrangement; Weight-balanced tree; K-ary tree; Combinatorics; Mathematics; Binary tree; Phylogenetic tree; Artificial intelligence; Tree structure; Binary search tree; Biology; Gene; Search algorithm; Botany","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":[0.0008069964,0.0005164897,0.0006374323,0.0004323499,0.0004917366,0.0008908927,0.002389794,0.0002842813,0.00001318568],"category_scores_gemma":[0.0001100629,0.0004678629,0.0001214222,0.0003671923,0.0007443788,0.00076577,0.003069363,0.0005976812,0.000005488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362979,"about_ca_system_score_gemma":0.0004171801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002914961,"about_ca_topic_score_gemma":0.0000708591,"domain_scores_codex":[0.9961923,0.00003290079,0.0004716521,0.001895591,0.0007497647,0.0006577463],"domain_scores_gemma":[0.9971265,0.00065321,0.0002585672,0.001413391,0.000321134,0.0002272469],"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":[0.000003477798,0.00002116814,0.00001518607,0.00005667661,0.000009735286,0.00005514471,0.0002674374,0.001030261,0.00009161118,0.04220625,0.0000573237,0.9561857],"study_design_scores_gemma":[0.0003764401,0.00013568,0.0001525415,0.0004479222,0.000007671952,0.00009647712,2.681079e-7,0.8393177,0.0005457841,0.1507324,0.007590961,0.0005961376],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002556639,0.001249258,0.9948792,0.0007065427,0.001529569,0.0004691569,0.00003090079,0.0001351769,0.0009745743],"genre_scores_gemma":[0.003021968,0.00008439893,0.9950116,0.0008925351,0.0005407474,0.00001551891,0.00003449057,0.00003249395,0.000366276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9555896,"threshold_uncertainty_score":0.9997773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652306749869173,"score_gpt":0.2811639945158672,"score_spread":0.2346409270171754,"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."}}