{"id":"W4390445556","doi":"10.1016/j.resconrec.2023.107401","title":"A water quality database for global lakes","year":2023,"lang":"en","type":"article","venue":"Resources Conservation and Recycling","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Threatened species; Water quality; Biodiversity; Aquatic ecosystem; Ecosystem; Environmental resource management; Environmental science; Anthropocene; Ecological footprint; Database; Geography; Ecology; Sustainability; Computer science; 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":[0.001294406,0.001081139,0.001155534,0.009679093,0.000625646,0.001682822,0.001194362,0.0008290728,0.01957531],"category_scores_gemma":[0.005085511,0.000538032,0.0006928889,0.01808294,0.0001925326,0.002112607,0.00155405,0.0006033792,0.009279076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001380646,"about_ca_system_score_gemma":0.006127141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05335544,"about_ca_topic_score_gemma":0.05455894,"domain_scores_codex":[0.9991417,0.00008324465,0.0003382354,0.0001402705,0.0002258794,0.0000706639],"domain_scores_gemma":[0.9960401,0.000711948,0.0007593615,0.0005430339,0.001566601,0.0003789268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005818854,0.0001925993,0.07958086,0.003395516,0.0004957165,0.0002470061,0.0004943621,0.006223925,0.00287737,0.004769279,0.7349047,0.1662368],"study_design_scores_gemma":[0.0004518442,0.00009335268,0.1400909,0.0008130761,0.000568679,0.0003141756,0.0004390992,0.008646991,0.0031009,0.004576527,0.8407462,0.000158371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007112462,0.0004926468,0.004999872,0.0002451449,0.00002184079,0.000185932,0.9800546,0.003238059,0.003649329],"genre_scores_gemma":[0.01802863,0.001060477,0.01500066,0.0001697593,0.00001697663,0.000659493,0.962324,0.0004342759,0.002305859],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05335544,"threshold_uncertainty_score":0.1060898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04358410582404867,"score_gpt":0.2991351077345155,"score_spread":0.2555510019104669,"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."}}