{"id":"W4225150334","doi":"10.3390/geographies2020016","title":"Geovisualization of Hydrological Flow in Hexagonal Grid Systems","year":2022,"lang":"en","type":"article","venue":"Geographies","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grid; Flow routing; Computer science; Watershed; Flow (mathematics); Terrain; Routing (electronic design automation); Computation; Hexagonal crystal system; Hexagonal tiling; Visualization; Computational science; Distributed computing; Hydrology (agriculture); Algorithm; Geology; Data mining; Geometry; Mathematics; Geography; Cartography; Machine learning; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003949401,0.0002259631,0.0001703127,0.0008680745,0.0002060068,0.0008773822,0.0002705852,0.0001856112,0.00459632],"category_scores_gemma":[0.001587896,0.0001129687,0.0002325442,0.0006854479,0.0002742282,0.0006974433,0.0006759595,0.0002352841,0.0003340005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954183,"about_ca_system_score_gemma":0.0003737456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446042,"about_ca_topic_score_gemma":0.003558508,"domain_scores_codex":[0.9998677,0.00004493306,0.000006456685,0.00002304505,0.00003414639,0.00002384891],"domain_scores_gemma":[0.9996235,0.0001774192,0.00002866204,0.00005740945,0.00008262192,0.00003029327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001094281,0.0002506544,0.02033302,0.000468302,0.00008002776,0.0008809572,0.003571132,0.3890023,0.08679329,0.04960896,0.01791374,0.4300032],"study_design_scores_gemma":[0.00008118399,0.0000976714,0.01157924,0.00004277908,0.00002051312,0.0001326578,0.00112558,0.9300392,0.02061657,0.01592188,0.02028972,0.00005297631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3822915,0.0002132303,0.5928426,0.0006068356,0.0001700666,0.0002213262,0.001852817,0.005551158,0.01625052],"genre_scores_gemma":[0.8537965,0.000207923,0.1431804,0.00007789563,0.00002418333,0.00009196433,0.0007346135,0.0002612804,0.001625078],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00459632,"threshold_uncertainty_score":0.01537621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008526646741355347,"score_gpt":0.2075604752957552,"score_spread":0.1990338285543999,"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."}}