{"id":"W2989578621","doi":"10.1002/lno.11382","title":"Linking metagenomics to aquatic microbial ecology and biogeochemical cycles","year":2019,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministério da Educação e Ciência; Bundesministerium für Bildung und Forschung; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; European Commission; Wisconsin Alumni Research Foundation; National Science Foundation","keywords":"Metagenomics; Biogeochemical cycle; Ecology; Microbiome; Microbial ecology; Microbial population biology; Ecosystem; Biology; Aquatic ecosystem; Bioinformatics","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":[0.004631672,0.0006663878,0.001061908,0.003042161,0.0003710386,0.00314546,0.0006436562,0.001047702,0.001183118],"category_scores_gemma":[0.003708326,0.0003549754,0.001414711,0.003466433,0.0007023089,0.00220241,0.001793991,0.002277086,0.0003976916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131762,"about_ca_system_score_gemma":0.001276628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008820721,"about_ca_topic_score_gemma":0.001519372,"domain_scores_codex":[0.9984988,0.0006104995,0.000109436,0.0002789933,0.0004096836,0.00009260592],"domain_scores_gemma":[0.9967758,0.001473718,0.000573141,0.0002746368,0.0006830571,0.0002197643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005701592,0.0001997064,0.08717325,0.02142146,0.006204299,0.001684846,0.002009151,0.01554687,0.2653073,0.05829285,0.03268658,0.5089034],"study_design_scores_gemma":[0.00004314219,0.0004699834,0.1380227,0.007560085,0.003155161,0.001948867,0.002212662,0.03074264,0.07287416,0.1410977,0.6014056,0.0004672604],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1043717,0.6987566,0.1506136,0.02070618,0.006049013,0.0001857141,0.006435532,0.001000119,0.01188158],"genre_scores_gemma":[0.3429469,0.4927786,0.1420966,0.007713525,0.004472526,0.0002242225,0.006399178,0.0003565346,0.00301203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004631672,"threshold_uncertainty_score":0.02449489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006425891982089195,"score_gpt":0.2025270945658481,"score_spread":0.1961012025837588,"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."}}