{"id":"W3113050553","doi":"10.3390/atmos11121363","title":"A Long-Term, 1-km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada","year":2020,"lang":"en","type":"article","venue":"Atmosphere","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Natural Resources Canada","funders":"Canadian Forest Service; Natural Resources Canada; Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Environment and Climate Change Canada; National Aeronautics and Space Administration","keywords":"Permafrost; Environmental science; Climatology; Precipitation; Anomaly (physics); Climate change; Climate model; Wind speed; Meteorology; Spatial distribution; Remote sensing; Geology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004066772,0.0005813983,0.0003122545,0.00152166,0.001211845,0.0007621261,0.001119527,0.0003133974,0.001794963],"category_scores_gemma":[0.001696351,0.0002786145,0.0005977352,0.004189361,0.0003106863,0.0004365926,0.0005121508,0.0006668072,0.000512743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01249865,"about_ca_system_score_gemma":0.02360612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9909295,"about_ca_topic_score_gemma":0.9935756,"domain_scores_codex":[0.9996073,0.00002425714,0.00002423094,0.00008205207,0.000176486,0.00008576806],"domain_scores_gemma":[0.9983872,0.00008220116,0.00008189389,0.0001179704,0.001174785,0.0001559123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003787917,0.0004927285,0.4195384,0.0007489158,0.0006923668,0.0006868346,0.001101501,0.2127989,0.007283462,0.005156905,0.238563,0.1125583],"study_design_scores_gemma":[0.000213459,0.00003171342,0.6708318,0.0001479393,0.0001040266,0.00007652492,0.0008957354,0.2198693,0.003394491,0.0007928325,0.1034852,0.0001570357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.351438,0.0005620312,0.005287017,0.0005758589,0.00007333089,0.0002761862,0.6321092,0.0019607,0.007717737],"genre_scores_gemma":[0.3602843,0.0004910183,0.01736113,0.0001106972,0.00001937964,0.0002461708,0.6180604,0.0001766843,0.003250312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01249865,"threshold_uncertainty_score":0.09068447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06432016423802031,"score_gpt":0.2408985325845485,"score_spread":0.1765783683465282,"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."}}