{"id":"W3024257532","doi":"10.1016/j.watres.2020.115902","title":"The magnitude and drivers of harmful algal blooms in China’s lakes and reservoirs: A national-scale characterization","year":2020,"lang":"en","type":"article","venue":"Water Research","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":231,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Youth Innovation Promotion Association; Major Science and Technology Program for Water Pollution Control and Treatment; China Postdoctoral Science Foundation","keywords":"Algal bloom; Environmental science; China; Magnitude (astronomy); Scale (ratio); Oceanography; Fishery; Ecology; Phytoplankton; Geography; Biology; Geology; Nutrient; Physics","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":[0.0004367114,0.0002164404,0.000246978,0.001175095,0.0005165777,0.0005465673,0.0003394309,0.0002252707,0.0006610558],"category_scores_gemma":[0.0006362931,0.000288942,0.0004515605,0.001759216,0.0004098182,0.0007333684,0.0008306064,0.0002360631,0.0000651528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033242,"about_ca_system_score_gemma":0.001463086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05076528,"about_ca_topic_score_gemma":0.1094434,"domain_scores_codex":[0.9997287,0.00002628856,0.0000342138,0.0000811197,0.00005953732,0.00007009086],"domain_scores_gemma":[0.9993811,0.00007782919,0.0002537152,0.00004698633,0.0001411854,0.00009910628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001471868,0.00001281863,0.9945632,0.00001691295,0.00005902776,0.00006778748,0.000382611,0.0003381701,0.001268612,0.0002664906,0.0001800519,0.002829662],"study_design_scores_gemma":[8.438262e-7,0.000004652625,0.9985953,0.00000233642,0.00001517725,0.00001830642,0.0002763994,0.0007091849,0.00006943357,0.00006284818,0.0002412806,0.00000414874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990807,0.00007756597,0.0001064612,0.00005525798,0.000002406258,0.00000427464,0.0002762878,0.000005086971,0.0003919945],"genre_scores_gemma":[0.9994456,0.00005690021,0.00005636273,0.00001277654,0.000002922386,0.000003986611,0.000255398,0.000001283109,0.000164715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05076528,"threshold_uncertainty_score":0.1009396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02487855598686043,"score_gpt":0.2707671632778528,"score_spread":0.2458886072909924,"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."}}