{"id":"W4376254943","doi":"10.1186/s40168-023-01537-7","title":"Genomic insights into cryptic cycles of microbial hydrocarbon production and degradation in contiguous freshwater and marine microbiomes","year":2023,"lang":"en","type":"article","venue":"Microbiome","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Parks Canada; Compute Canada; Canada First Research Excellence Fund; ArcticNet; Université Laval","keywords":"Biology; Metagenomics; Water column; Anoxic waters; Deltaproteobacteria; Phototroph; Ecology; Microbial ecology; Microbial mat; Proteobacteria; Cyanobacteria; Gammaproteobacteria; Botany; Bacteria; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001531613,0.0003168979,0.0002693553,0.00129081,0.0004307739,0.0005729287,0.0001392073,0.0002373787,0.0005683947],"category_scores_gemma":[0.0003680326,0.000151272,0.000321204,0.001509543,0.0002318462,0.0001926977,0.0004817342,0.0002047389,0.0001542001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007302316,"about_ca_system_score_gemma":0.001058336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02375187,"about_ca_topic_score_gemma":0.03699056,"domain_scores_codex":[0.9998604,0.00000718546,0.000006663042,0.00005719182,0.00002742675,0.00004112334],"domain_scores_gemma":[0.99972,0.00003773125,0.00009441537,0.00001386852,0.00006943558,0.00006452104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005204197,0.00005032095,0.2327489,0.0002864604,0.0001254899,0.0002613605,0.000930433,0.0008778318,0.7479054,0.000441816,0.0002516525,0.01559995],"study_design_scores_gemma":[0.00000813703,0.00009707996,0.9860821,0.00002409222,0.00007847872,0.0002635791,0.000609592,0.001059136,0.009098362,0.0002218788,0.002444852,0.00001268762],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948398,0.0005082643,0.0009542686,0.0000329146,0.00000323394,0.0000136608,0.002979632,0.00003200978,0.0006362506],"genre_scores_gemma":[0.9907175,0.0003917545,0.002955161,0.00007047839,0.000006568465,0.00001991536,0.005336973,0.00001347104,0.0004881097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02375187,"threshold_uncertainty_score":0.0472272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006739898790560005,"score_gpt":0.1961907812526917,"score_spread":0.1894508824621317,"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."}}