{"id":"W4403406654","doi":"10.1128/msystems.00317-24","title":"Marine biofilms: cyanobacteria factories for the global oceans","year":2024,"lang":"en","type":"article","venue":"mSystems","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Japan Science and Technology Agency; Ministry of Education, Culture, Sports, Science and Technology; Japan Society for the Promotion of Science; Hong Kong University of Science and Technology; Research Grants Council, University Grants Committee; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","keywords":"Cyanobacteria; Metagenomics; Ecology; Marine habitats; Biology; Seawater; Water column; Ecological niche; Geomicrobiology; Biofilm; Oceanography; Habitat; Microbial ecology; Geology; Paleontology; Environmental biotechnology; Bacteria","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":[0.0004028482,0.0004692567,0.0005457753,0.0008246868,0.0008010521,0.00124224,0.0003168889,0.0004927032,0.00122262],"category_scores_gemma":[0.000813627,0.0001822368,0.0004658428,0.001149233,0.0004957989,0.001545891,0.001393882,0.0007880796,0.0002975145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006368798,"about_ca_system_score_gemma":0.0007653495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004408244,"about_ca_topic_score_gemma":0.005846623,"domain_scores_codex":[0.9997801,0.00003012271,0.0000119301,0.00008914362,0.00005356174,0.00003520184],"domain_scores_gemma":[0.9997858,0.00002605561,0.00006799258,0.00002816106,0.00003494468,0.00005711125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003388654,0.00004959929,0.5405025,0.001724273,0.0007094476,0.001679213,0.002553322,0.007115929,0.2059097,0.02041532,0.009122489,0.2098793],"study_design_scores_gemma":[0.00002696245,0.0001290811,0.8216214,0.0005969039,0.0006067193,0.0009780176,0.004203392,0.0143658,0.00977235,0.03808367,0.1094649,0.0001507936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9237799,0.04050263,0.02082251,0.004502834,0.0002379735,0.00004130918,0.003882695,0.0006369079,0.005593163],"genre_scores_gemma":[0.9782601,0.008682236,0.009551903,0.0007194145,0.0001024441,0.00002088931,0.001782999,0.00008010347,0.0008000148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004408244,"threshold_uncertainty_score":0.008765161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127335031238998,"score_gpt":0.2481041683394925,"score_spread":0.2353706652155927,"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."}}