{"id":"W1699715884","doi":"10.1002/ppp.1752","title":"High‐Resolution Mapping of Wet Terrain within Discontinuous Permafrost using LiDAR Intensity","year":2012,"lang":"en","type":"article","venue":"Permafrost and Periglacial Processes","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Geological Survey of Canada; Natural Resources Canada","funders":"","keywords":"Permafrost; Lidar; Terrain; Geology; Elevation (ballistics); Remote sensing; Peat; Hydrology (agriculture); Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00008135097,0.00008385022,0.00009693268,0.0006264088,0.0001843474,0.0002954074,0.0001777648,0.00006714812,0.0005079025],"category_scores_gemma":[0.0001934748,0.00007238896,0.00005023691,0.000604053,0.0001211574,0.000107119,0.0002038613,0.00008421443,0.0001209871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004018006,"about_ca_system_score_gemma":0.0004689352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1777929,"about_ca_topic_score_gemma":0.3482294,"domain_scores_codex":[0.999958,0.000004288496,0.000001187684,0.00000734667,0.00001631462,0.00001278795],"domain_scores_gemma":[0.9999151,0.00001178826,0.0000136522,0.000005011981,0.00004134621,0.00001301354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001893847,0.00008319099,0.7388909,0.00006918555,0.00004433317,0.0002887878,0.0008994127,0.007615508,0.08241253,0.000271675,0.0008834653,0.1683517],"study_design_scores_gemma":[0.00001028506,0.00002173089,0.9751427,0.00001499165,0.00001211019,0.00009001768,0.000469548,0.02004116,0.003130528,0.0001027109,0.0009562674,0.000008064544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962965,0.00004582663,0.001829504,0.00001313601,8.490444e-7,0.000008254386,0.000294475,0.00004043603,0.001471197],"genre_scores_gemma":[0.9970303,0.00003258007,0.002504413,0.000003619614,0.000001094733,0.000003546701,0.0002019582,0.00000233494,0.0002202187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1777929,"threshold_uncertainty_score":0.353516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120997063421824,"score_gpt":0.2505587024756282,"score_spread":0.2093487318414099,"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."}}