{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003068648,0.0001147415,0.000116715,0.00003575211,0.001534543,0.0001108678,0.0001844005,0.00002990339,0.00003831349],"category_scores_gemma":[0.0001792281,0.00007251533,0.00002723659,0.0005639562,0.001420298,0.0001472398,0.0001277221,0.00003499409,0.0001573938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849458,"about_ca_system_score_gemma":0.00004818774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01328512,"about_ca_topic_score_gemma":0.1742799,"domain_scores_codex":[0.9985675,0.0001087579,0.0002514399,0.0004806522,0.0001975593,0.0003940797],"domain_scores_gemma":[0.9991188,0.0000961957,0.0001176047,0.0004031402,0.00003126954,0.0002329872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004806627,0.000009471696,0.9199358,0.00001240375,0.00004979346,0.00003952573,0.0003777047,0.001160653,0.0002247559,0.005173638,0.05835494,0.01465647],"study_design_scores_gemma":[0.0001129792,0.00002715177,0.5458046,0.00003977717,0.00003891513,0.00006512601,0.0001734943,0.0004213405,0.00007506583,0.002938827,0.4501078,0.0001949505],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9031884,0.00005184969,0.03583894,0.04369191,0.002914352,0.002087778,0.0003555459,0.0001511186,0.0117201],"genre_scores_gemma":[0.9915445,0.000002672411,0.00255842,0.0004749003,0.00005479279,0.0001299714,0.00006957148,0.000007216125,0.005157984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3917529,"threshold_uncertainty_score":0.9997653,"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."}}