{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001938364,0.0001755453,0.0002166587,0.0002760323,0.00006615803,0.0000306413,0.0001068199,0.0001071963,0.0001746234],"category_scores_gemma":[0.0000215276,0.0001583881,0.00002705396,0.0005093834,0.0003156992,0.000158208,0.0002850468,0.00007707383,0.000148791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012376,"about_ca_system_score_gemma":0.000009350685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010322,"about_ca_topic_score_gemma":0.004556575,"domain_scores_codex":[0.9988672,0.00005404918,0.0003454385,0.0004216089,0.00007608469,0.0002356577],"domain_scores_gemma":[0.9996554,0.00001541654,0.0001121389,0.0001512738,0.000009715822,0.00005603299],"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":[0.00003139302,0.00004054204,0.05381632,0.00004014538,0.000008138246,0.000003880256,0.0008988137,0.000008190946,0.9417076,0.000002335796,0.00099727,0.002445403],"study_design_scores_gemma":[0.0006022569,0.00005022885,0.273704,0.00003833038,0.0000129555,0.00001918907,0.00007423674,0.0001069916,0.7219126,0.0001678334,0.003068055,0.0002433655],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989185,0.00009191323,0.000002214454,0.0003761418,0.0001725081,0.0003230418,0.0000194967,0.0000425173,0.0000536192],"genre_scores_gemma":[0.9982359,0.0002990492,0.0008121043,0.0000662377,0.00002321292,0.000003557157,0.0001313317,0.00001649898,0.0004121117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2198876,"threshold_uncertainty_score":0.6458879,"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."}}