{"id":"W2884318471","doi":"10.3390/geosciences8080278","title":"Measuring Hyperscale Topographic Anisotropy as a Continuous Landscape Property","year":2018,"lang":"en","type":"article","venue":"Geosciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anisotropy; Landform; Triangulated irregular network; Geology; Robustness (evolution); Remote sensing; Computer science; Digital elevation model; Geomorphology; Optics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002091374,0.0002427626,0.0002092373,0.001177321,0.0001357612,0.0007117643,0.0002265862,0.000184297,0.0006746687],"category_scores_gemma":[0.0009266978,0.0001094589,0.0002114454,0.001081313,0.0003000957,0.0005418189,0.0003512354,0.0002220492,0.0001675001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002204907,"about_ca_system_score_gemma":0.000207251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002080936,"about_ca_topic_score_gemma":0.00449217,"domain_scores_codex":[0.9998362,0.00002178496,0.000008148008,0.0000511408,0.00006028704,0.00002246472],"domain_scores_gemma":[0.9996006,0.000138789,0.00008836093,0.00007228968,0.00007596237,0.00002406713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003147733,0.0001430635,0.07477572,0.0002701743,0.0001438844,0.0002990446,0.0004424413,0.1066688,0.4116677,0.005102449,0.001207126,0.398965],"study_design_scores_gemma":[0.00001657328,0.0000802841,0.1679201,0.00001606529,0.00004649155,0.0004226361,0.0002631621,0.7538492,0.07196324,0.003019378,0.002332175,0.00007076767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6930897,0.0001228265,0.301569,0.0000676078,0.00001410419,0.0000431332,0.0007319288,0.0009944325,0.003367248],"genre_scores_gemma":[0.9273869,0.00009041897,0.07174065,0.00001462611,0.00001062369,0.00002070946,0.000332918,0.00004761275,0.0003555591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002080936,"threshold_uncertainty_score":0.004137635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655126514570078,"score_gpt":0.2190151215315053,"score_spread":0.2024638563858046,"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."}}