{"id":"W3023970327","doi":"10.1101/2020.05.04.077222","title":"Progress in quickly finding orthologs as reciprocal best hits","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Bottleneck; Reciprocal; Software; Computer science; Genome; Computational biology; Biology; Gene; Genetics; Programming language; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01639785,0.002846318,0.00226525,0.003766532,0.001237412,0.004888374,0.00363051,0.001844234,0.0120837],"category_scores_gemma":[0.03204997,0.001532613,0.002048156,0.002742544,0.0009616924,0.005225011,0.004012898,0.00332138,0.009905277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066165,"about_ca_system_score_gemma":0.001607159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002143923,"about_ca_topic_score_gemma":0.001938361,"domain_scores_codex":[0.9915678,0.003127436,0.0006325975,0.002054353,0.002272815,0.0003449816],"domain_scores_gemma":[0.970988,0.01669515,0.001171828,0.004332223,0.005904166,0.0009085474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002291923,0.000764076,0.03817033,0.00522843,0.001221738,0.0004914894,0.001544789,0.02276583,0.180298,0.02257098,0.1013436,0.6233089],"study_design_scores_gemma":[0.000724974,0.0009524093,0.02818505,0.001481151,0.0009051261,0.003723857,0.001996577,0.2739306,0.3427371,0.06822899,0.2762502,0.0008839153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1761295,0.01020301,0.7234589,0.004298272,0.0009104506,0.0002285122,0.009761726,0.06311691,0.01189267],"genre_scores_gemma":[0.1364734,0.002076739,0.8379226,0.0004315808,0.0001445252,0.0001326394,0.01258375,0.008097452,0.002137325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01639785,"threshold_uncertainty_score":0.08672112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194413909618882,"score_gpt":0.2483344117313485,"score_spread":0.2288930207694603,"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."}}