{"id":"W1997780525","doi":"10.1007/s10817-007-9082-1","title":"Inferring Phylogenetic Trees Using Answer Set Programming","year":2007,"lang":"en","type":"article","venue":"Journal of Automated Reasoning","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Austrian Science Fund; National Science Foundation","keywords":"Cladistics; Phylogenetic tree; Computer science; Taxon; Set (abstract data type); Formalism (music); Phylogenetics; Theoretical computer science; Representation (politics); Artificial intelligence; Programming language; Biology; Paleontology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936728,0.0002528098,0.0004071772,0.0004525445,0.0002730371,0.0003500698,0.0008414992,0.0001496094,0.000009029505],"category_scores_gemma":[0.0003463748,0.0002054778,0.0002091831,0.0008348884,0.00006319574,0.0007125559,0.0001936592,0.0003947648,0.000009359329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001815149,"about_ca_system_score_gemma":0.0002214642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002460523,"about_ca_topic_score_gemma":0.00001859276,"domain_scores_codex":[0.9976709,0.0001016907,0.0007693358,0.0002687712,0.000511317,0.000677955],"domain_scores_gemma":[0.9980382,0.0001791473,0.0007935509,0.0003144578,0.000394965,0.0002797109],"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":[0.0002011122,0.0009882543,0.2400466,0.000263767,0.001173577,0.005924863,0.03117246,0.03687591,0.1597115,0.01549846,0.001238671,0.5069049],"study_design_scores_gemma":[0.00226641,0.0007836327,0.1021864,0.001463708,0.000159122,0.007137577,0.0008026409,0.8507912,0.02678573,0.0005427835,0.006034627,0.001046149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.739432,0.00128332,0.2566566,0.00003653147,0.0009098304,0.0001002244,3.213003e-7,0.0004161592,0.001164913],"genre_scores_gemma":[0.8451947,0.0000182659,0.1543531,0.00004143908,0.0003409506,5.711451e-7,4.607996e-7,0.00002127841,0.00002920244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8139153,"threshold_uncertainty_score":0.8379143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376857054380687,"score_gpt":0.2980693273525292,"score_spread":0.2743007568087223,"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."}}