{"id":"W2028532360","doi":"10.1016/j.coldregions.2012.03.004","title":"Permafrost probability modeling above and below treeline, Yukon, Canada","year":2012,"lang":"en","type":"article","venue":"Cold Regions Science and Technology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Queen's University","funders":"Office of Polar Programs; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Australian Government","keywords":"Permafrost; Elevation (ballistics); Physical geography; Vegetation (pathology); Normalized Difference Vegetation Index; Snow; Geology; Lapse rate; Climate change; Hydrology (agriculture); Environmental science; Climatology; Geomorphology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000414494,0.0001237505,0.0001500136,0.0001910324,0.0006325666,0.00004997041,0.0002405113,0.00009733073,0.0001028815],"category_scores_gemma":[0.0001230984,0.00009919419,0.00001043426,0.0009279717,0.0010407,0.0003688954,0.00006272397,0.0001531509,0.000006815698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000020108,"about_ca_system_score_gemma":0.0003487171,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1334745,"about_ca_topic_score_gemma":0.7731739,"domain_scores_codex":[0.9987199,0.00001246843,0.0001495246,0.0003251143,0.0002349496,0.0005580024],"domain_scores_gemma":[0.9992989,0.00004601318,0.00003951313,0.0002528791,0.0001358234,0.0002268452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004930061,0.0000168761,0.9863187,0.00001839653,0.000003074761,0.000005614424,0.0002372484,0.00003799073,0.001405697,0.003729428,0.001021219,0.007200806],"study_design_scores_gemma":[0.001419097,0.0004944541,0.554434,0.0001422831,0.0001043913,0.001094928,0.009604128,0.2923971,0.00256494,0.01229222,0.1234673,0.001985088],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897091,0.002755769,0.00003411285,0.005666934,0.0002348164,0.0001844957,0.0003227574,0.00005001123,0.001042018],"genre_scores_gemma":[0.9988712,0.0004127238,0.0001748597,0.0003658649,0.00006280299,0.000003881312,0.00003768767,0.000002331689,0.00006862375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6396994,"threshold_uncertainty_score":0.8722958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176701595726934,"score_gpt":0.2232960810955666,"score_spread":0.1915290651382973,"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."}}