{"id":"W2031260681","doi":"10.1186/1471-2164-15-567","title":"Enhanced whole genome sequence and annotation of Clostridium stercorarium DSM8532T using RNA-seq transcriptomics and high-throughput proteomics","year":2014,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Bundesministerium für Verkehr, Innovation und Technologie; Genome Canada; Austrian Centre of Industrial Biotechnology; Austrian Federal Ministry of Economy, Family and Youth; McGill University","keywords":"Biology; Proteomics; Computational biology; Transcriptome; DNA microarray; RNA-Seq; Whole genome sequencing; Genome; Genetics; Genome project; Annotation; Proteogenomics; Genomics; Sequence (biology); Gene; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001865248,0.000173745,0.0002177286,0.00008139597,0.00006539283,0.00004188115,0.00008481923,0.0001415804,0.000004087672],"category_scores_gemma":[0.00001193632,0.0001882127,0.00003152191,0.00009836286,0.0001050101,0.0001953864,0.00003240857,0.0001290463,0.000003635165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008624533,"about_ca_system_score_gemma":0.00003574556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004367949,"about_ca_topic_score_gemma":0.00000911504,"domain_scores_codex":[0.9991375,0.00003264687,0.000290693,0.0002659171,0.00008365738,0.0001896521],"domain_scores_gemma":[0.9995758,0.00001549438,0.0000836067,0.0001966989,0.00005653459,0.00007186816],"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":[0.00004065128,0.000009104455,0.00005905182,0.00039996,0.00001184704,1.542821e-7,0.0004790983,0.004886549,0.9929162,0.00007919103,0.000008085267,0.001110095],"study_design_scores_gemma":[0.0008510022,0.0001063693,0.0005774169,0.00002446881,0.00004190756,0.00001289411,0.0002265363,0.09006032,0.9046434,0.000470257,0.002660592,0.0003248809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8904759,0.0002317839,0.1082901,0.00005469623,0.0004529099,0.0003193742,0.00006359511,0.00006326511,0.00004844646],"genre_scores_gemma":[0.9644974,0.0002742098,0.03492836,0.00003843075,0.0001492282,0.000004491911,0.00004011211,0.00003169843,0.00003607917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08827286,"threshold_uncertainty_score":0.7675092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516274625689506,"score_gpt":0.2165714640770642,"score_spread":0.1914087178201691,"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."}}