{"id":"W2008480759","doi":"10.1002/ppp.672","title":"Using the MODIS land surface temperature product for mapping permafrost: an application to northern Québec and Labrador, Canada","year":2009,"lang":"en","type":"article","venue":"Permafrost and Periglacial Processes","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Université Laval; Center for Northern Studies","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Public Security of the People's Republic of China; ArcticNet; Japan Aerospace Exploration Agency; National Aeronautics and Space Administration","keywords":"Permafrost; Land cover; Moderate-resolution imaging spectroradiometer; Snow; Remote sensing; Vegetation (pathology); Spectroradiometer; Geology; Snow cover; Spatial distribution; Climatology; Physical geography; Land use; Geomorphology; Satellite; Geography; Reflectivity; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001702295,0.0002532492,0.000233554,0.00003595282,0.0009503883,0.0002874842,0.0001856716,0.00006682749,0.00010278],"category_scores_gemma":[0.00005273871,0.0001783188,0.00002205108,0.0002736056,0.00008019466,0.0003307944,0.00001486668,0.0001278032,0.000002349363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001689891,"about_ca_system_score_gemma":0.0005158877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6888933,"about_ca_topic_score_gemma":0.9942477,"domain_scores_codex":[0.9987108,0.00003253612,0.0002056821,0.0004691272,0.0002067132,0.0003751631],"domain_scores_gemma":[0.9992616,0.00008393382,0.0000701856,0.0001917189,0.000177522,0.0002150602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001360502,0.00001846844,0.970497,0.0001875024,0.000009744704,0.000002776292,0.008538463,0.0008713553,0.009564,0.000003941662,0.0002819048,0.009888786],"study_design_scores_gemma":[0.0005286291,0.000235649,0.9266904,0.00006292552,0.00005605538,0.00009830757,0.003762312,0.01126675,0.0007956076,0.0001044232,0.05562313,0.0007758387],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831634,0.003415684,0.00003770952,0.004132086,0.0001169909,0.000687367,0.008339048,0.00002954561,0.00007820864],"genre_scores_gemma":[0.9950155,0.0001898246,0.000107641,0.001672086,0.0006108031,0.000007721093,0.002294745,0.00001060461,0.00009108838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3053544,"threshold_uncertainty_score":0.7309715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02619872128507897,"score_gpt":0.2488363766200956,"score_spread":0.2226376553350166,"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."}}