{"id":"W4378574680","doi":"10.1111/tgis.13067","title":"<scp>MultiscaleDTM</scp>: An open‐source R package for multiscale geomorphometric analysis","year":2023,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; Dalhousie University","funders":"National Oceanic and Atmospheric Administration; National Fish and Wildlife Foundation","keywords":"Terrain; Geodetic datum; Raster graphics; Curvature; Surface finish; Measure (data warehouse); Ellipse; Position (finance); Standard deviation; Geology; Landform; Geodesy; Data mining; Computer science; Remote sensing; Geometry; Geography; Mathematics; Computer graphics (images); Statistics; Cartography; Engineering; Geomorphology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004579618,0.0001657691,0.0002878192,0.0005166269,0.0003021543,0.00009669419,0.0005048934,0.0001623244,0.002046648],"category_scores_gemma":[0.00003854182,0.0001430157,0.0001741185,0.005728174,0.0001054468,0.0003686927,0.00004539912,0.0002109512,0.0005096541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001249165,"about_ca_system_score_gemma":0.000008561966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002474735,"about_ca_topic_score_gemma":0.004835675,"domain_scores_codex":[0.9984611,0.00005922233,0.0003000629,0.0004816432,0.0002468052,0.000451147],"domain_scores_gemma":[0.9990805,0.0002714478,0.00005910969,0.000412707,0.000009526979,0.0001666459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005271007,0.00124393,0.1306303,0.0000348212,0.0006567435,0.00005348528,0.004256803,0.713072,0.005482943,0.00002208342,0.007739642,0.1367545],"study_design_scores_gemma":[0.002941926,0.000288501,0.4970636,0.0000169993,0.0007763289,0.0000114919,0.001355528,0.3741242,0.002189866,0.000192768,0.120709,0.0003297947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7573857,0.0000360926,0.2368751,0.0002021216,0.0002074156,0.0007697554,0.0002458831,0.000216526,0.00406136],"genre_scores_gemma":[0.9688362,0.0001313034,0.003343659,0.00005566118,0.00001947384,0.000172074,0.0001164888,0.00003539471,0.02728976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3664333,"threshold_uncertainty_score":0.9988656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995650819563902,"score_gpt":0.2736376149263958,"score_spread":0.2536811067307568,"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."}}