{"id":"W2071405080","doi":"10.4296/cwrj267","title":"A Comparison of Data Sources for Manual and Automated Hydrographical Network Delineation","year":2004,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Groundwater and Watershed Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital elevation model; Computer science; STREAMS; Tributary; Elevation (ballistics); Remote sensing; Data mining; Cartography; Geography; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01318694,0.0004555525,0.0005212685,0.01064383,0.0005900895,0.002192618,0.001189284,0.0004721275,0.002081823],"category_scores_gemma":[0.04467066,0.0003295618,0.0004353084,0.005607845,0.0004020628,0.001766607,0.001728417,0.0003198726,0.0006991849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00164009,"about_ca_system_score_gemma":0.001218439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02087686,"about_ca_topic_score_gemma":0.02738011,"domain_scores_codex":[0.9909704,0.003681811,0.0007512231,0.0007891851,0.003564816,0.0002426195],"domain_scores_gemma":[0.936882,0.03105281,0.003577104,0.00770306,0.02038374,0.0004012613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002215767,0.0004652808,0.2333604,0.0009335727,0.0005711678,0.0003385377,0.003599994,0.02085033,0.01262492,0.002043851,0.006835423,0.7161608],"study_design_scores_gemma":[0.000639202,0.0009855527,0.6545648,0.001060775,0.0006345595,0.000499574,0.006730952,0.2219861,0.06630011,0.002345689,0.04388681,0.0003657926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9126729,0.0005614549,0.05945415,0.0002770712,0.00009849034,0.0008811361,0.01091504,0.004209634,0.01093005],"genre_scores_gemma":[0.8758363,0.0002842016,0.1116046,0.00008977987,0.00004410412,0.0005956049,0.009548195,0.0004288353,0.001568267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02087686,"threshold_uncertainty_score":0.06974006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907650078448562,"score_gpt":0.2730522459092727,"score_spread":0.2439757451247871,"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."}}