{"id":"W4387835421","doi":"10.1016/j.isprsjprs.2023.10.008","title":"Identifying active retrogressive thaw slumps from ArcticDEM","year":2023,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Cooperative Institute for Research in Environmental Sciences; Colorado State University; Natural Sciences and Engineering Research Council of Canada; Stanford University; University of Colorado Boulder; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Permafrost; Crowdsourcing; Elevation (ballistics); Arctic; Digital elevation model; Computer science; Feature (linguistics); Remote sensing; False positive paradox; Artificial intelligence; Geology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003449584,0.0003194282,0.0003093781,0.002588457,0.0009901286,0.0009457417,0.0004508052,0.0004262371,0.001035108],"category_scores_gemma":[0.0005323242,0.0002109758,0.0003204299,0.001591386,0.0002198789,0.000394257,0.000757053,0.0002588441,0.0006474223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158185,"about_ca_system_score_gemma":0.0005653821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02282603,"about_ca_topic_score_gemma":0.07904901,"domain_scores_codex":[0.9998612,0.000006638641,0.000009747317,0.00003280944,0.00004045595,0.00004908385],"domain_scores_gemma":[0.9995698,0.00005094194,0.00007216238,0.00003766195,0.0002072188,0.00006211988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005752682,0.0001274163,0.8577036,0.0001223501,0.0001221332,0.001390642,0.001499566,0.005534297,0.0463837,0.0003503469,0.002877414,0.08331329],"study_design_scores_gemma":[0.00002558613,0.00009784304,0.9441565,0.00007619979,0.0001693164,0.0005894678,0.00273546,0.03252339,0.009775159,0.0002524475,0.009567911,0.00003060133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928393,0.0002650921,0.00210575,0.00003932404,0.00004482564,0.00002015829,0.001127944,0.0001992495,0.003358176],"genre_scores_gemma":[0.9939165,0.0001491156,0.003279655,0.00002655315,0.00002783961,0.00001193223,0.001677871,0.00003943964,0.0008712256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02282603,"threshold_uncertainty_score":0.04538631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399598015395539,"score_gpt":0.280031889856503,"score_spread":0.2360359097025476,"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."}}