{"id":"W2127234426","doi":"10.1109/iccabs.2011.5729939","title":"Invited: Fast and theoretically strong algorithms for kinship discovery","year":2011,"lang":"en","type":"article","venue":"","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic logic; Simple (philosophy); Kinship; Inheritance (genetic algorithm); Inference; Population; Domain (mathematical analysis); Theoretical computer science; Task (project management); Algorithm; Mathematics; Artificial intelligence; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000150927,0.0001372572,0.0001055472,0.00003151947,0.00005142109,0.00003565726,0.000131004,0.00009873394,0.00002912225],"category_scores_gemma":[0.00003330983,0.0001139418,0.00005931147,0.00003503178,0.0001249164,0.000006103988,0.0001155977,0.00004281984,0.000003356854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005104425,"about_ca_system_score_gemma":0.00001083037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153386,"about_ca_topic_score_gemma":0.00001213665,"domain_scores_codex":[0.9992056,0.0000210373,0.0001323926,0.0003150269,0.00007427148,0.0002516301],"domain_scores_gemma":[0.9995996,0.00001061999,0.00003254576,0.0002298613,0.00004499799,0.000082412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008463205,0.000731949,0.0396403,0.0002456988,0.0008492642,0.00001477119,0.001154686,0.00001019174,0.7137412,0.08206401,0.0103594,0.1503422],"study_design_scores_gemma":[0.007632404,0.005532496,0.06493933,0.00005716718,0.0003256918,0.00008586283,0.003108426,0.00281639,0.8278707,0.02272034,0.06231641,0.002594774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5220056,0.0005315835,0.4621392,0.0005130541,0.0002129217,0.0008112313,0.00008722101,0.00003894731,0.0136603],"genre_scores_gemma":[0.961462,0.0000906281,0.03610504,0.0005830713,0.000200807,0.00006443817,0.0001061562,0.0000344295,0.001353474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4394564,"threshold_uncertainty_score":0.4646412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405946729272449,"score_gpt":0.2524882149054544,"score_spread":0.2184287476127299,"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."}}