{"id":"W4367596319","doi":"10.1016/j.renene.2023.04.146","title":"A predictive model of velocity for local hydrokinetic power assessment based on remote sensing data","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Software deployment; Remote sensing; Hydropower; STREAMS; Renewable energy; Environmental science; Elevation (ballistics); Random forest; Arctic; Computer science; Scale (ratio); Data stream mining; Meteorology; Data mining; Machine learning; Engineering; Geology; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004114501,0.0001583654,0.0001925198,0.00008129278,0.00009904411,0.00001660835,0.0003604495,0.00006045315,0.00007764639],"category_scores_gemma":[0.00002093599,0.0001464314,0.00005588289,0.0003284913,0.00009240997,0.0001165306,0.0004746936,0.00004942815,0.000008397182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001866398,"about_ca_system_score_gemma":0.00004926057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005731702,"about_ca_topic_score_gemma":0.001363001,"domain_scores_codex":[0.9984383,0.00004602822,0.000223563,0.0005021392,0.0004601318,0.0003298224],"domain_scores_gemma":[0.9989567,0.00009658757,0.0001001536,0.0007578574,0.000011609,0.00007705381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004442862,0.00005070378,0.00003505425,0.00001278497,0.00002083783,0.000003015882,0.00001662029,0.9691837,0.002174556,0.00005919992,0.01713429,0.01126478],"study_design_scores_gemma":[0.0005455952,0.0002099996,0.0003855352,0.00003291749,0.00003682166,1.750194e-7,0.00004644017,0.9875959,0.003059079,0.002372092,0.005561996,0.0001534144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004997042,0.000004256592,0.9745404,0.0002235064,0.0001245349,0.0002001573,0.00006665423,0.00008652007,0.01975699],"genre_scores_gemma":[0.9470952,0.00002808843,0.04965858,0.0002397571,0.00002532143,0.000005712549,0.0002446562,0.0000289011,0.002673758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9420982,"threshold_uncertainty_score":0.8664657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02482701569438413,"score_gpt":0.2688450476730125,"score_spread":0.2440180319786283,"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."}}