{"id":"W4364360285","doi":"10.1080/01431161.2023.2195571","title":"A transfer learning approach for automatic mapping of retrogressive thaw slumps (RTSs) in the western Canadian Arctic","year":2023,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thermokarst; Permafrost; Landform; Arctic; Computer science; Remote sensing; Ecosystem; Satellite imagery; Transfer of learning; Physical geography; Environmental science; Artificial intelligence; Geology; Geomorphology; Oceanography; Ecology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007649832,0.00007719724,0.000155338,0.0004250527,0.00006312844,0.00006606768,0.0002279236,0.0000418375,0.00005356191],"category_scores_gemma":[0.0001203005,0.00005618533,0.00009957085,0.0002311772,0.00003573646,0.0001507703,0.000004303939,0.0002096557,0.000005114787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002064943,"about_ca_system_score_gemma":0.0000879969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03787937,"about_ca_topic_score_gemma":0.09709883,"domain_scores_codex":[0.9989588,0.00008449175,0.0003270792,0.00008312122,0.0003494995,0.0001970599],"domain_scores_gemma":[0.9992875,0.0002791546,0.0001289216,0.00005260996,0.0001929311,0.00005889361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001529158,0.00001711079,0.3348528,0.0002133004,0.0002610308,0.0009574555,0.03113114,0.03395265,0.00128911,0.0000157978,0.0004695368,0.5966871],"study_design_scores_gemma":[0.0007637119,0.0001314645,0.2587591,0.0006982274,0.00003082269,0.001177286,0.007705784,0.7277583,0.00009384684,0.0003738785,0.002352738,0.0001547673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946125,0.0001265747,0.001856436,0.002200736,0.0003930109,0.000112663,0.00008969974,0.000006094492,0.0006023043],"genre_scores_gemma":[0.9975855,0.00007299722,0.001557999,0.0003517274,0.0002443771,2.036848e-8,0.0001579958,0.000004417298,0.00002491599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6938057,"threshold_uncertainty_score":0.9685275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05086450828178037,"score_gpt":0.270688532880474,"score_spread":0.2198240245986936,"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."}}