{"id":"W2904785470","doi":"10.1093/femsle/fny285","title":"Decoding the ocean's microbiological secrets for marine enzyme biodiscovery","year":2018,"lang":"en","type":"review","venue":"FEMS Microbiology Letters","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Fundação para a Ciência e a Tecnologia; ERAB: The European Foundation for Alcohol Research; European Commission; Ministerio de Ciencia, Innovación y Universidades","keywords":"Abundance (ecology); Seawater; Ecology; Population; Microbial population biology; Biology; Marine life; Microbial ecology; Oceanography; Biodiversity; Marine bacteriophage; Environmental science; Bacteria; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001110421,0.001049019,0.001127135,0.002762439,0.0004216458,0.00176477,0.000948053,0.001893556,0.003369149],"category_scores_gemma":[0.001548322,0.0004453562,0.0005030207,0.002589176,0.0009568321,0.003628585,0.001474456,0.002675062,0.003670168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122484,"about_ca_system_score_gemma":0.001644999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001052311,"about_ca_topic_score_gemma":0.001845556,"domain_scores_codex":[0.9996564,0.00005041398,0.00004248259,0.00006515125,0.0001354667,0.0000501062],"domain_scores_gemma":[0.9993512,0.000230649,0.00007612529,0.00003480274,0.0002225843,0.00008464397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007142729,0.00004825045,0.0003037963,0.01308562,0.00007085386,0.0002367223,0.0001171163,0.000439274,0.007092993,0.01596502,0.03759596,0.9249729],"study_design_scores_gemma":[0.000003893308,0.00003551592,0.0003812475,0.001224721,0.00003434953,0.0003375855,0.00005532844,0.00007952577,0.001418882,0.002710873,0.9937027,0.0000154926],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001622707,0.9970758,0.0003859267,0.0006755439,0.0004999893,0.000003965296,0.00002861697,0.00001306948,0.001154956],"genre_scores_gemma":[0.00129835,0.9965676,0.0004540288,0.0003297357,0.0002633691,0.000005866605,0.00005480831,0.000004600818,0.001021661],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003369149,"threshold_uncertainty_score":0.01127094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597131656028852,"score_gpt":0.2761654170492052,"score_spread":0.2401941004889167,"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."}}