{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001918502,0.0001555329,0.000271862,0.0001127407,0.00019403,0.00001150831,0.0002117109,0.0002973365,0.0007195126],"category_scores_gemma":[0.00001806424,0.0001513618,0.00005150513,0.0001790497,0.0005500383,0.00006302574,0.0005143236,0.0002985942,0.0003708037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001157523,"about_ca_system_score_gemma":0.000005421501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000355611,"about_ca_topic_score_gemma":0.000473191,"domain_scores_codex":[0.9989453,0.0001625579,0.0001726953,0.0003516538,0.00002752745,0.0003402911],"domain_scores_gemma":[0.9994035,0.0002131125,0.00005179884,0.0002338023,0.000003408215,0.00009435997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007834554,0.00005063252,0.8343615,0.00001011037,0.00004170218,0.000002663909,0.0005321594,0.00001191744,0.1629563,0.0004846449,0.00067172,0.0007983284],"study_design_scores_gemma":[0.00126794,0.001061075,0.9176437,0.00001704943,0.0001022381,0.000158345,0.0002644291,0.0001325301,0.01073468,0.02284344,0.0451051,0.0006694719],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977877,0.0001216251,0.00001307532,0.0007053897,0.0001973303,0.0002048126,0.000009140509,0.00003163143,0.0009292565],"genre_scores_gemma":[0.9954357,0.0001156861,0.001379447,0.002950727,0.00002271864,0.000004159212,0.00003097561,0.000009064032,0.00005156719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1522216,"threshold_uncertainty_score":0.7878158,"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."}}