{"id":"W2372965849","doi":"","title":"The Extraction and Analysis of Landform Characters Based on High-resolution DEM","year":2012,"lang":"en","type":"article","venue":"Geomatics & Spatial Information Technology","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Initiatives Ontario North","funders":"","keywords":"Landform; Terrain; Character (mathematics); Geography; Extraction (chemistry); Base (topology); Digital elevation model; Remote sensing; Geology; Computer science; Cartography; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003042604,0.00007208699,0.0001293724,0.0003356065,0.0001777663,0.00003141881,0.00006829605,0.0001114455,0.00003418733],"category_scores_gemma":[0.00009239497,0.00004616692,0.00003160801,0.0004279133,0.00007141197,0.000296943,0.000005217179,0.00009889967,0.00003093623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005624291,"about_ca_system_score_gemma":0.00001129453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357979,"about_ca_topic_score_gemma":0.0009463443,"domain_scores_codex":[0.9993346,0.00002149436,0.0002765942,0.00004263021,0.0001492126,0.0001755124],"domain_scores_gemma":[0.9993846,0.0001366955,0.0002391613,0.0001589772,0.00004392323,0.00003664102],"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":[0.00005688217,0.00001365528,0.1631005,0.00001572869,0.00008669162,1.306183e-7,0.0002590058,0.00893106,0.00001283914,0.0006638854,0.00005082515,0.8268088],"study_design_scores_gemma":[0.0001026627,0.00005488562,0.5037042,0.000004724347,0.00006836768,0.000001533026,0.0001026571,0.4944208,0.0001791477,0.0001287439,0.001187692,0.00004457694],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765989,0.00003805699,0.02156065,0.0006305448,0.0002389976,0.0001060288,0.00003105256,0.00006293772,0.0007328874],"genre_scores_gemma":[0.9986275,0.00005885032,0.0009407423,0.00009473124,0.00002050542,3.848835e-7,0.0002510335,9.572339e-7,0.000005280069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8267642,"threshold_uncertainty_score":0.2052866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005966474760440136,"score_gpt":0.2015674071888404,"score_spread":0.1956009324284003,"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."}}