{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002517158,0.00008707459,0.0000862365,0.00003389573,0.0004858187,0.00008163557,0.0002875744,0.00003340238,0.0007445107],"category_scores_gemma":[0.00003855659,0.00005376907,0.00003814462,0.0005098163,0.0007873868,0.0001399356,0.00008949859,0.00005771107,0.001391724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001653182,"about_ca_system_score_gemma":0.00001526467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002019268,"about_ca_topic_score_gemma":0.0003726758,"domain_scores_codex":[0.9989191,0.00002785578,0.0001079936,0.0003401703,0.0003237031,0.0002812232],"domain_scores_gemma":[0.9995926,0.00001464907,0.00004160522,0.0002357159,0.00001486131,0.0001005839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004195128,0.0003328494,0.3778947,0.000008852627,0.00001852972,0.0000161075,0.004474859,0.00006665765,0.1685234,0.001048299,0.02064408,0.4269297],"study_design_scores_gemma":[0.0004671093,0.0006569125,0.2714104,0.00003254857,0.00002850416,0.0001565254,0.002194923,0.007883626,0.01838524,0.003799445,0.6942942,0.0006905739],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8370081,0.00003163372,0.0005972191,0.0005981189,0.0001424795,0.00011924,0.000001305142,0.00007997479,0.1614219],"genre_scores_gemma":[0.9914827,0.00001465414,0.003849436,0.0002335251,0.00009194743,0.000002997026,7.295517e-7,0.000005194919,0.004318784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6736501,"threshold_uncertainty_score":0.9993858,"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."}}