{"id":"W2782701038","doi":"10.3390/a11010003","title":"Analytic Combinatorics for Computing Seeding Probabilities","year":2018,"lang":"en","type":"article","venue":"Algorithms","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministerio de Economía y Competitividad; Generalitat de Catalunya; Centres de Recerca de Catalunya","keywords":"Heuristics; Seeding; Heuristic; Computer science; Set (abstract data type); Algorithm; Estimator; Function (biology); Simple (philosophy); Generating function; Sequence (biology); Construct (python library); Enumerative combinatorics; Theoretical computer science; Mathematics; Mathematical optimization; Artificial intelligence; Discrete mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001639686,0.0001075988,0.0001210252,0.00002159211,0.0001809701,0.00002173509,0.0001204943,0.00006081139,0.000002500304],"category_scores_gemma":[0.00006355692,0.0001033788,0.00006798544,0.00006011696,0.0001120899,4.434705e-7,0.0001043294,0.0000274925,0.00000403794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112424,"about_ca_system_score_gemma":0.00002875966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000091973,"about_ca_topic_score_gemma":0.000006019592,"domain_scores_codex":[0.9993135,0.00001064974,0.0001389338,0.0002503611,0.00005871994,0.0002278027],"domain_scores_gemma":[0.9995356,0.00002243449,0.00004755666,0.0001698135,0.000183978,0.0000406769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002406209,0.0005035495,0.06366017,0.0003828451,0.001596272,0.000005821757,0.003859257,0.0003369954,0.7939622,0.01974813,0.02580281,0.08990128],"study_design_scores_gemma":[0.005163501,0.007243541,0.03001767,0.00008371197,0.0002977074,0.00008689187,0.003011647,0.04205842,0.4286581,0.04300383,0.4381089,0.002266144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829201,0.0007012206,0.01325597,0.0001226374,0.0007023748,0.0002950694,0.00002193406,0.000008938096,0.0019718],"genre_scores_gemma":[0.9903727,0.00003216785,0.00801887,0.0001158574,0.0008806154,0.00001396877,0.00001539902,0.00001707651,0.0005334011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4123061,"threshold_uncertainty_score":0.4215667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910573574937198,"score_gpt":0.271745095904013,"score_spread":0.252639360154641,"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."}}