{"id":"W1985103004","doi":"10.1186/1471-2164-15-737","title":"Characterization of the core and accessory genomes of Pseudomonas aeruginosa using bioinformatic tools Spine and AGEnt","year":2014,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Antibiotic Resistance in Bacteria","field":"Biochemistry, Genetics and Molecular Biology","cited_by":208,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Université Laval; National Institutes of Health; American Cancer Society","keywords":"Genome; Biology; Whole genome sequencing; Pseudomonas aeruginosa; Comparative genomics; Genetics; Computational biology; Bacterial genome size; Gene; Genomics; Bacteria","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001131247,0.00008436527,0.0001349545,0.00001868622,0.00003654756,0.00001698117,0.000109046,0.00006428955,0.000001977724],"category_scores_gemma":[0.00004191611,0.00006608081,0.00003065979,0.00003284916,0.0001294915,0.000007062802,0.0001407915,0.00002321332,1.891838e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007426579,"about_ca_system_score_gemma":0.00004086334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003310222,"about_ca_topic_score_gemma":0.000007459235,"domain_scores_codex":[0.9994947,0.00002226181,0.0002365037,0.0001098558,0.00005010413,0.00008662266],"domain_scores_gemma":[0.9994573,0.000007564283,0.0002442517,0.0002288823,0.00003822001,0.00002377978],"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":[0.00001830767,0.000007318515,0.0458819,0.0001421561,0.00001177299,2.78019e-8,0.00007454547,0.00001510961,0.9526359,0.00008246221,0.000001706894,0.001128814],"study_design_scores_gemma":[0.0002982667,0.00005251461,0.4691669,0.00003559544,0.00002960479,0.000008761547,0.00006658601,0.001250167,0.5282111,0.00003604848,0.0007382056,0.0001062415],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949511,0.0001506539,0.004575375,0.000009359821,0.00007112473,0.000165726,0.00002536597,0.000001687122,0.00004961856],"genre_scores_gemma":[0.9956177,0.0003029162,0.003917876,0.00003779848,0.0000437076,7.286575e-7,0.00003447945,0.000008941429,0.00003579432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4244247,"threshold_uncertainty_score":0.2694697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763810017796833,"score_gpt":0.2485182932183866,"score_spread":0.2208801930404183,"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."}}