{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003429129,0.0001304894,0.0002536483,0.0006391216,0.0002933001,0.0008174501,0.0002498757,0.0002382816,0.001191566],"category_scores_gemma":[0.001043392,0.0001451869,0.0002730625,0.0004755013,0.000608632,0.0006007671,0.0005209565,0.0001298712,0.00005712442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005071287,"about_ca_system_score_gemma":0.0003158705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01218263,"about_ca_topic_score_gemma":0.01841374,"domain_scores_codex":[0.9998716,0.00002817664,0.000008916364,0.0000534453,0.00001191605,0.0000259373],"domain_scores_gemma":[0.9993367,0.0002708609,0.0001710264,0.00005878965,0.0000806779,0.00008189285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001856962,0.00006569319,0.9191921,0.000134062,0.0002229092,0.0003038445,0.0004109438,0.01807136,0.04081228,0.001960667,0.0002657904,0.01837455],"study_design_scores_gemma":[0.000009402009,0.00004206028,0.972611,0.000006606781,0.00004146352,0.00005729261,0.0002777356,0.02383743,0.001437334,0.001170931,0.0004954422,0.00001341894],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989453,0.00005442847,0.0005123075,0.00002087246,0.000001036869,0.000004568349,0.00005453634,0.00001058304,0.0003962787],"genre_scores_gemma":[0.9998103,0.0000111865,0.0001231529,0.000003039625,7.727119e-7,0.000001053695,0.00001662058,0.000001766645,0.00003212387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01218263,"threshold_uncertainty_score":0.02422345,"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."}}