{"id":"W4416852850","doi":"10.5194/tc-19-6355-2025","title":"Monitoring Arctic permafrost – examining the contribution of volunteered geographic information to mapping ice-wedge polygons","year":2025,"lang":"en","type":"article","venue":"The cryosphere","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polygon (computer graphics); Permafrost; Arctic; Centroid; Geographic information system; Geocoding; The arctic","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":[0.001643571,0.0001682552,0.0001905414,0.00126822,0.0004065838,0.0006116749,0.0003484145,0.0001685375,0.0004784453],"category_scores_gemma":[0.004276777,0.00006732322,0.0001246037,0.001075682,0.0003557267,0.0004887541,0.0006671167,0.0001108094,0.0001037016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005781713,"about_ca_system_score_gemma":0.0007002792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0389079,"about_ca_topic_score_gemma":0.07742695,"domain_scores_codex":[0.9993336,0.0003022697,0.0000195261,0.0001058411,0.0001832306,0.00005554182],"domain_scores_gemma":[0.9971998,0.001213786,0.0002806229,0.0003857119,0.0006789042,0.0002412194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004450207,0.0002031253,0.7390831,0.0002462557,0.0001158341,0.0005493574,0.007771072,0.02745656,0.00666737,0.001098771,0.002378862,0.2139847],"study_design_scores_gemma":[0.00006063029,0.0007369395,0.6863968,0.000252611,0.0001267447,0.0007158819,0.02560051,0.2474446,0.01183125,0.002709277,0.02405227,0.00007248652],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895836,0.00008218038,0.006960591,0.00005668756,0.00001069119,0.00007631099,0.0006357306,0.0001108923,0.002483278],"genre_scores_gemma":[0.9884133,0.00004938256,0.01053575,0.000008776263,0.000003639379,0.00003523838,0.0006309533,0.000009299057,0.0003136405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0389079,"threshold_uncertainty_score":0.07736284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02174398772995267,"score_gpt":0.232014664398156,"score_spread":0.2102706766682033,"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."}}