{"id":"W2613887458","doi":"10.1002/2016wr020102","title":"Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":272,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biogeochemical cycle; Wetland; Environmental science; Nutrient; Lake ecosystem; Ecosystem; Hydrology (agriculture); Residence time (fluid dynamics); Biogeochemistry; Ecology; Phosphorus; Nutrient cycle; Biology; Geology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001002638,0.0001503577,0.0002157144,0.0001681221,0.0004119941,0.0001970806,0.0009590644,0.0001194428,0.0001799175],"category_scores_gemma":[0.00004264114,0.00008576018,0.00005705655,0.00007448322,0.0007568774,0.0001956105,0.001485883,0.0003301235,0.0002883816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007859106,"about_ca_system_score_gemma":0.000004354374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001868037,"about_ca_topic_score_gemma":0.0001146076,"domain_scores_codex":[0.9976625,0.0001042659,0.0002828757,0.0004277663,0.0006639968,0.0008585554],"domain_scores_gemma":[0.9992014,0.00002470015,0.00004140535,0.0005740297,0.00003383991,0.0001246258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001613832,0.0001664935,0.9378938,0.00004598412,0.000005423303,0.00002233162,0.01192709,0.0000544034,0.04499345,0.000003922055,0.0000797332,0.004645988],"study_design_scores_gemma":[0.001482718,0.0002094156,0.1163969,0.0001757557,0.000008763252,0.00001200554,0.001896063,0.006820078,0.7762202,0.02631854,0.07000957,0.000449923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900585,0.000113543,0.000001766784,0.0005620485,0.00002337834,0.0002005233,0.000008294034,0.00001898281,0.009012952],"genre_scores_gemma":[0.999154,0.00004421467,0.0001099854,0.00001241579,0.00005058386,0.00003789704,0.00002098352,0.00001954033,0.000550363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8214968,"threshold_uncertainty_score":0.3706658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02847494536563144,"score_gpt":0.282946436982044,"score_spread":0.2544714916164126,"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."}}