{"id":"W2999088805","doi":"10.1002/lno.11403","title":"Impacts of deep‐sea mining on microbial ecosystem services","year":2020,"lang":"en","type":"article","venue":"Limnology and Oceanography","topic":"Geochemistry and Elemental Analysis","field":"Earth and Planetary Sciences","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; McGill University","funders":"Division of Ocean Sciences; Center for Dark Energy Biosphere Investigations; Carnegie Institution of Washington; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Ecosystem services; Ecosystem; Environmental science; Deep sea; Biomass (ecology); Carbon sequestration; Biodiversity; Biogeochemical cycle; Lead (geology); Environmental resource management; Oceanography; Ecology; Geology; Biology","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":[0.0006128399,0.0004617313,0.0002116021,0.0006303705,0.0004750161,0.001198969,0.0002154468,0.0003158028,0.001123882],"category_scores_gemma":[0.0007619624,0.0001015611,0.0003184589,0.0006486662,0.0003876921,0.0008573846,0.001636199,0.0002993225,0.0001466939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00147301,"about_ca_system_score_gemma":0.0009131234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01352763,"about_ca_topic_score_gemma":0.02726263,"domain_scores_codex":[0.9997337,0.00006352948,0.00002339326,0.00002917498,0.00006890906,0.0000813536],"domain_scores_gemma":[0.9994286,0.00007351135,0.000142058,0.00002938614,0.0002229721,0.0001035306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005118539,0.000116279,0.8173107,0.0006274278,0.0003034054,0.0006435527,0.0007673549,0.008056668,0.08912621,0.002154821,0.00103866,0.0793431],"study_design_scores_gemma":[0.00001342437,0.0004552821,0.9585996,0.0001324306,0.0001241602,0.0002859814,0.003599165,0.007095409,0.01715047,0.004063801,0.008443474,0.00003683484],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922862,0.0009386666,0.0007089952,0.0006852424,0.00001738717,0.00001463509,0.0005137526,0.00001716733,0.004817897],"genre_scores_gemma":[0.9986173,0.0004549932,0.0003575786,0.00007082383,0.000003921951,0.000004129516,0.0001128604,0.000002534645,0.0003757758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01352763,"threshold_uncertainty_score":0.02689779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006088138276352078,"score_gpt":0.1761273069055056,"score_spread":0.1700391686291536,"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."}}