{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01048791,0.002227149,0.002019009,0.009466494,0.002231986,0.006464858,0.003516919,0.002205049,0.006102988],"category_scores_gemma":[0.1004845,0.001386793,0.002552011,0.004749884,0.00602528,0.009158369,0.003928042,0.0045651,0.002023139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004402557,"about_ca_system_score_gemma":0.002698898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001527997,"about_ca_topic_score_gemma":0.001387209,"domain_scores_codex":[0.9942557,0.002292563,0.0004430264,0.0008755922,0.001738743,0.0003943812],"domain_scores_gemma":[0.934334,0.05533325,0.00328378,0.003717321,0.002557783,0.0007738365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003540322,0.00004164622,0.001800983,0.0001910585,0.00005045849,0.0001573734,0.0001955162,0.1640302,0.001003375,0.8050553,0.001726184,0.02571252],"study_design_scores_gemma":[0.000008870014,0.00001342155,0.0001653458,0.00004013859,0.00001181562,0.00006846543,0.00002707694,0.4103727,0.0008414472,0.587015,0.001410355,0.00002549272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005589866,0.0001783701,0.9915379,0.0001687898,0.00002990402,0.00005361375,0.00007865454,0.0003759175,0.001987033],"genre_scores_gemma":[0.2822074,0.001296808,0.7082542,0.0004362272,0.000337085,0.0008733214,0.0009906694,0.001418923,0.004185325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01048791,"threshold_uncertainty_score":0.05546606,"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."}}