{"id":"W2295565233","doi":"10.3133/sir20165009","title":"Network global navigation satellite system surveys to harmonize American and Canadian datum for the Lake Champlain Basin","year":2016,"lang":"en","type":"article","venue":"Scientific investigations report","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; New York State Department of Environmental Conservation; U.S. Department of State","keywords":"Hydrology (agriculture); Shore; Snowmelt; Geology; Water level; Shelf ice; Geological survey; Water year; Spring (device); Flooding (psychology); Flood myth; Geodetic datum; Bay; Snowpack; Flood stage; North American Datum of 1927; Surface water; Seiche; Snow; Drainage basin; Oceanography; Environmental science; Geomorphology; Cryosphere; Geography; Ice shelf; Archaeology; 100-year flood","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0007541864,0.0007309588,0.0004875738,0.00364132,0.001665077,0.00103952,0.001188899,0.0003765185,0.03245712],"category_scores_gemma":[0.001815697,0.0003893544,0.0002383012,0.007933665,0.0002504216,0.00065439,0.0009559504,0.0008762338,0.007954127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009572944,"about_ca_system_score_gemma":0.02761034,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9572245,"about_ca_topic_score_gemma":0.9802188,"domain_scores_codex":[0.9992533,0.00005665397,0.00004240014,0.00009689048,0.0003960784,0.0001546582],"domain_scores_gemma":[0.9967129,0.00005264673,0.0001695258,0.00009833886,0.002739089,0.0002274576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00004324617,0.00002838061,0.01619864,0.0001007935,0.00002927132,0.00003002405,0.0001479789,0.0003641244,0.0002925805,0.001165221,0.9559659,0.02563376],"study_design_scores_gemma":[0.00005901149,0.00001270062,0.1596236,0.0001540128,0.000023395,0.00002068657,0.0004241285,0.001152494,0.0002654392,0.0003424361,0.8378872,0.00003502082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01240525,0.000699502,0.001836943,0.001564855,0.0003683672,0.0005040292,0.8845533,0.001102978,0.09696476],"genre_scores_gemma":[0.03790748,0.0008769469,0.01153465,0.0008842557,0.00007350686,0.00093568,0.8799626,0.0004796587,0.06734516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04277551,"threshold_uncertainty_score":0.1085799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960969788176402,"score_gpt":0.2351448208201312,"score_spread":0.2155351229383672,"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."}}