{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001557519,0.00008571278,0.0001274989,0.00003929029,0.0001045057,0.00002471447,0.0003241213,0.00005310914,0.00001979633],"category_scores_gemma":[0.00004921898,0.0000515159,0.0000377795,0.00009629899,0.00003986121,0.0002212561,0.00005708994,0.0000318922,0.000005469959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009345322,"about_ca_system_score_gemma":0.0002532781,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005204719,"about_ca_topic_score_gemma":0.03740131,"domain_scores_codex":[0.9991667,0.00001916519,0.0002142552,0.0002447641,0.0001662895,0.0001888849],"domain_scores_gemma":[0.9993284,0.00008775658,0.0001589577,0.0002579226,0.0001099078,0.00005707939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009344806,0.00007969717,0.05631143,0.0000884801,0.00002824655,0.00000357078,0.0003294746,0.00001868832,0.1381388,0.005021518,0.00761541,0.7922713],"study_design_scores_gemma":[0.005388598,0.001007157,0.3417894,0.0004061996,0.00005383075,0.0002584486,0.00007155143,0.03885582,0.4987892,0.02456745,0.08735601,0.001456396],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5542849,0.00003134356,0.4412726,0.002048801,0.000422398,0.0001457561,0.00001479342,0.0000682063,0.001711207],"genre_scores_gemma":[0.9857372,0.000006246009,0.01273352,0.0001128879,0.00007404156,0.00003147621,0.000003030245,0.000005302113,0.001296316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7908149,"threshold_uncertainty_score":0.9801636,"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."}}