{"id":"W3050332539","doi":"10.1080/15230430.2020.1800972","title":"Meteorological drivers of interannual variation in transparency of mountain lakes","year":2020,"lang":"en","type":"article","venue":"Arctic Antarctic and Alpine Research","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Franklin and Marshall College; Alberta Ingenuity Centre for Water Research; National Science Foundation","keywords":"Environmental science; Precipitation; Transparency (behavior); Climate change; Drainage basin; Hydrology (agriculture); Ecosystem; Physical geography; Climatology; Geography; Meteorology; Ecology; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002899971,0.0001030954,0.0001824116,0.0006252773,0.0005700798,0.0006499075,0.0001998221,0.0001741384,0.0006443546],"category_scores_gemma":[0.001068949,0.0001433804,0.0001952068,0.0009659542,0.0002991035,0.000261876,0.0004151626,0.0001797616,0.00005610824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008174794,"about_ca_system_score_gemma":0.0005494543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.23472,"about_ca_topic_score_gemma":0.3895422,"domain_scores_codex":[0.9998416,0.00002853366,0.00001413451,0.00003914923,0.00002737087,0.00004914649],"domain_scores_gemma":[0.9990773,0.0001607536,0.0003560312,0.0000583208,0.0002083128,0.000139302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004943494,0.000007614003,0.9969463,0.000005839974,0.00003538918,0.00003603702,0.0004003108,0.0001534263,0.00139169,0.00002470996,0.00007925359,0.0008700736],"study_design_scores_gemma":[2.760229e-7,0.000001520691,0.9997953,6.859319e-7,0.000001865531,0.000004309998,0.00007135925,0.00007998849,0.00001331641,0.000002766566,0.00002785111,7.598405e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999453,0.00003926505,0.00004306492,0.00001252301,9.242801e-7,0.000002108692,0.0002037177,0.000003579328,0.00024181],"genre_scores_gemma":[0.9997003,0.00001729152,0.000032535,0.000003476876,0.000001209093,0.000002640788,0.0001783966,0.000001304139,0.00006290332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.23472,"threshold_uncertainty_score":0.4667076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07253620077252529,"score_gpt":0.2973728161295169,"score_spread":0.2248366153569916,"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."}}