{"id":"W3206535601","doi":"","title":"The Role of Lake to River Connectivity in Runoff Generation in Canada and Alaska","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Surface runoff; Hydrology (agriculture); Geography; Geology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004698813,0.0001204816,0.0002134764,0.0009156901,0.002372837,0.001981976,0.0004941746,0.000334953,0.001960079],"category_scores_gemma":[0.002683879,0.0001952608,0.0002112417,0.001054494,0.0009161413,0.0006887377,0.001098708,0.00029191,0.0001043288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009425852,"about_ca_system_score_gemma":0.009760953,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9388995,"about_ca_topic_score_gemma":0.9777974,"domain_scores_codex":[0.999811,0.00004103724,0.0000155599,0.00003436171,0.00003913254,0.00005891249],"domain_scores_gemma":[0.9987395,0.0003255823,0.0001252867,0.00003074643,0.0005013251,0.0002775369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001408027,0.00004053621,0.9812634,0.00001988157,0.00006338219,0.0003481489,0.001862268,0.005823656,0.0008132383,0.00180616,0.0005201291,0.007298405],"study_design_scores_gemma":[0.00000833041,0.00001393624,0.9895369,0.00001908494,0.00004383102,0.00005477785,0.004971593,0.003748461,0.0001958197,0.0005513027,0.0008435522,0.00001242845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960293,0.00005866095,0.00006399309,0.0001169714,0.000002137076,0.000004423789,0.0001839257,0.000006385607,0.00353429],"genre_scores_gemma":[0.9991249,0.00005330383,0.00007821896,0.000009960038,8.737063e-7,0.000002081538,0.00006399828,0.000004094333,0.0006625793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06110048,"threshold_uncertainty_score":0.1229206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00933210914992823,"score_gpt":0.1899766796103283,"score_spread":0.1806445704604,"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."}}