{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004667501,0.0004897765,0.0006324496,0.0005419584,0.0004335158,0.0007498783,0.001048181,0.0007433504,0.001388414],"category_scores_gemma":[0.001573864,0.0004894128,0.0004753277,0.0006881873,0.0004032133,0.0009410685,0.0004032993,0.0008768798,0.0004015312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008433753,"about_ca_system_score_gemma":0.001109382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06052878,"about_ca_topic_score_gemma":0.0355182,"domain_scores_codex":[0.9998882,0.00001854022,0.000007782594,0.00004191137,0.00002806405,0.00001558256],"domain_scores_gemma":[0.9995943,0.0002134439,0.00004056249,0.00003156117,0.0000961999,0.00002411615],"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":[0.00001239084,0.00001322767,0.0004456775,0.000004632359,0.00000600227,0.000008909959,0.000004258508,0.9939083,0.0002413722,0.0003265591,0.0001696804,0.004858958],"study_design_scores_gemma":[0.00000168617,0.000002167537,0.00009667764,7.071737e-7,0.000001418175,9.777414e-7,6.879585e-7,0.9996508,0.00006211662,0.0001568425,0.00002421103,0.000001608255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2668262,0.0003284379,0.7238477,0.0004513143,0.0001504345,0.00008392167,0.00105753,0.002328692,0.004925659],"genre_scores_gemma":[0.978851,0.0001180899,0.01869754,0.00002846649,0.00002687028,0.00007045409,0.0003938953,0.00004401217,0.001769672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06052878,"threshold_uncertainty_score":0.1203529,"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."}}