{"id":"W3040770432","doi":"","title":"A hydrologically explicit, spatially exact, classification of landforms for Canada at 1:500,000 scale.","year":2016,"lang":"en","type":"article","venue":"EGUGA","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Landform; Scale (ratio); Geology; Geography; Physical geography; Geomorphology; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.0002057954,0.000363565,0.0002407425,0.00153284,0.001129461,0.0008854264,0.0008607587,0.0003792766,0.003861401],"category_scores_gemma":[0.001045767,0.0002919534,0.0003932557,0.002404049,0.0003278643,0.0005295688,0.0007250189,0.0005054264,0.001087898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005618345,"about_ca_system_score_gemma":0.01532352,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9835871,"about_ca_topic_score_gemma":0.9914585,"domain_scores_codex":[0.9998252,0.000008301271,0.000006386597,0.0000340683,0.00007448557,0.00005152752],"domain_scores_gemma":[0.9995381,0.00001931166,0.00002234058,0.0000444142,0.0003178337,0.00005811656],"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":[0.000509097,0.000564796,0.2506662,0.0003013721,0.000232305,0.000359272,0.0007633397,0.1112606,0.02080101,0.006505926,0.1059044,0.5021318],"study_design_scores_gemma":[0.0001319591,0.00004071774,0.5922413,0.0001053706,0.0001125113,0.0001432343,0.001517728,0.3397329,0.008061731,0.00245088,0.05533497,0.0001266945],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7733355,0.0009694841,0.05475938,0.001405807,0.0001093617,0.0004517543,0.1400793,0.007332944,0.02155652],"genre_scores_gemma":[0.8551316,0.0004267467,0.08120008,0.0001126335,0.00001377065,0.0001018087,0.05374279,0.0004021568,0.008868366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01641291,"threshold_uncertainty_score":0.04076415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330841718044556,"score_gpt":0.2141104561154549,"score_spread":0.2008020389350094,"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."}}