{"id":"W4378473062","doi":"10.5311/josis.2023.26.240","title":"Surface network and drainage network: towards a common data structure","year":2023,"lang":"en","type":"article","venue":"Journal of Spatial Information Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Terrain; Drainage; Drainage network; Triangulated irregular network; Digital elevation model; Geology; Computer science; Remote sensing; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002189487,0.00007903106,0.0001370052,0.0000597672,0.0004691025,0.0001113765,0.0006538797,0.00003220386,0.0001034927],"category_scores_gemma":[0.00009162694,0.00005872409,0.00001485092,0.0007453294,0.0005077864,0.003997249,0.001258794,0.0001428095,0.00006253349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003875972,"about_ca_system_score_gemma":0.00002213978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001819781,"about_ca_topic_score_gemma":0.0001192422,"domain_scores_codex":[0.9987359,0.00002673827,0.0003418968,0.00009902674,0.0005037228,0.0002927164],"domain_scores_gemma":[0.999342,0.00003466007,0.000304346,0.0002114779,0.0000269145,0.00008060036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00007202834,0.00001012488,0.104726,0.00001947602,0.00003035176,0.00001621697,0.003172757,0.7584355,0.0002036009,0.0006275972,0.05584276,0.07684357],"study_design_scores_gemma":[0.0005087844,0.0001729468,0.7077151,0.00003049488,0.0000305415,0.00004322555,0.0003713625,0.2212175,0.00007669068,0.007292564,0.06233434,0.0002064772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768823,0.00003323047,0.007208041,0.003590024,0.0009779397,0.0001798308,0.00001434857,0.00003614199,0.01107808],"genre_scores_gemma":[0.9979684,0.00007599252,0.001181378,0.0006390846,0.00009264478,2.46526e-7,0.000007316625,0.000001840308,0.00003305443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6029891,"threshold_uncertainty_score":0.3608004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366392206877442,"score_gpt":0.2735285670843902,"score_spread":0.2498646450156157,"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."}}