{"id":"W3133357792","doi":"10.1101/2021.02.12.431019","title":"CompMap: an allele-specific expression read-counter based on competitive mapping","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Compute Canada","keywords":"Python (programming language); Allele; Biology; Computational biology; Population; Genetics; Computer science; Gene; Programming language","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":[0.003350428,0.001303932,0.001087532,0.001545719,0.0008816041,0.001533568,0.00337539,0.0009288799,0.01460754],"category_scores_gemma":[0.00791259,0.0008981773,0.001500358,0.001287096,0.0009289542,0.001356665,0.002559584,0.002007175,0.004986099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006244792,"about_ca_system_score_gemma":0.001893405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001887028,"about_ca_topic_score_gemma":0.003117788,"domain_scores_codex":[0.9974467,0.0003816029,0.0001234938,0.0008069156,0.001061119,0.0001801879],"domain_scores_gemma":[0.9967237,0.001400948,0.0003313391,0.0006983537,0.0005900097,0.0002555521],"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":[0.002390123,0.0007601432,0.02554956,0.001634021,0.001086514,0.001044525,0.0007738674,0.06150134,0.1732431,0.02611422,0.1778041,0.5280986],"study_design_scores_gemma":[0.0003510663,0.000289041,0.008834404,0.00008798698,0.0001560899,0.0006740476,0.00008845375,0.7256184,0.1655633,0.02778594,0.07015936,0.0003919475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03139549,0.0002177636,0.7668362,0.000323292,0.0003929285,0.000299123,0.008503735,0.1867305,0.005301025],"genre_scores_gemma":[0.1518407,0.00009845314,0.8065009,0.0006825809,0.0001228571,0.001312334,0.01003706,0.02260928,0.006795817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01460754,"threshold_uncertainty_score":0.04886711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843540024751312,"score_gpt":0.2123011346275786,"score_spread":0.1938657343800655,"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."}}