{"id":"W7014464970","doi":"","title":"PermaSAR – Improving TanDEM-X D-InSAR techniques for the detection of small-scale vertical movements&#13;\\nin arctic permafrost regions","year":2016,"lang":"en","type":"other","venue":"Helmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Permafrost; Tundra; Subsidence; Vegetation (pathology); Arctic; Radar; Lidar; Active layer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00148316,0.0008265211,0.000350421,0.001024694,0.0003453311,0.000877712,0.0008200782,0.0004301142,0.002925311],"category_scores_gemma":[0.000783985,0.0003347895,0.0004534739,0.001017059,0.0002928991,0.0008280243,0.0009216623,0.0007540873,0.003253374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002783073,"about_ca_system_score_gemma":0.000663745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007593447,"about_ca_topic_score_gemma":0.01067134,"domain_scores_codex":[0.9992241,0.0001255224,0.00003586922,0.0002184575,0.0003339681,0.00006205469],"domain_scores_gemma":[0.9995021,0.00004590748,0.0000400722,0.0001280477,0.0002600627,0.00002381222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003363482,0.0001749363,0.01589902,0.0003223521,0.0001705622,0.0002954156,0.0003805498,0.01729031,0.107261,0.007715893,0.04798138,0.8021722],"study_design_scores_gemma":[0.0001846464,0.0005148767,0.07079993,0.0002136299,0.0001448991,0.000746954,0.0004998763,0.238831,0.09535714,0.006448952,0.5860073,0.0002507127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548805,0.005911587,0.6863581,0.002375829,0.001840667,0.0004500039,0.01641572,0.02219434,0.1095733],"genre_scores_gemma":[0.2650468,0.003630752,0.6597312,0.00112926,0.0005617343,0.0002970839,0.0301193,0.001128732,0.03835506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007593447,"threshold_uncertainty_score":0.01509851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748662879891595,"score_gpt":0.2736947802698305,"score_spread":0.2562081514709145,"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."}}