{"id":"W3081182371","doi":"10.1101/2020.08.26.269076","title":"A Phased <i>Canis lupus familiaris</i> Labrador Retriever Reference Genome Utilizing High Molecular Weight DNA Extraction Methods and High Resolution Sequencing Technologies","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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Directorate; U.S. Department of Homeland Security","keywords":"Reference genome; Genotyping; Genome; Biology; Computational biology; DNA sequencing; Genomics; Whole genome sequencing; Population; Genome-wide association study; Genetics; Single-nucleotide polymorphism; Genotype; Gene; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001594664,0.0007268044,0.0007884296,0.001323805,0.0007577502,0.001207762,0.001691333,0.001072729,0.006487187],"category_scores_gemma":[0.001959705,0.0005381159,0.0010621,0.001307984,0.0003470825,0.0005019526,0.0007219074,0.0008878321,0.005155134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007919653,"about_ca_system_score_gemma":0.0009717858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004995727,"about_ca_topic_score_gemma":0.009658351,"domain_scores_codex":[0.998587,0.0001883167,0.0001112859,0.0005194098,0.0004930573,0.0001008671],"domain_scores_gemma":[0.9986058,0.0001958909,0.0002679443,0.000409808,0.000448265,0.00007219942],"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":[0.0004708777,0.0001270399,0.002636076,0.0003616738,0.0001268046,0.0003306928,0.0001799989,0.00151921,0.946565,0.001564956,0.006912169,0.0392055],"study_design_scores_gemma":[0.000292843,0.001282079,0.03595529,0.0002333866,0.0005907523,0.002536874,0.0001721522,0.01154142,0.6717134,0.001307629,0.2742211,0.0001529288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3818087,0.00212827,0.4749246,0.001323847,0.0005750699,0.002271472,0.09835276,0.0149254,0.02368977],"genre_scores_gemma":[0.1797668,0.0009780232,0.6355485,0.001212163,0.0001250988,0.001189646,0.1637211,0.003592294,0.01386626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006487187,"threshold_uncertainty_score":0.02170181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02046354001707588,"score_gpt":0.2544675750187252,"score_spread":0.2340040350016493,"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."}}