{"id":"W1970330059","doi":"10.1016/j.inpa.2014.04.002","title":"Uncertainty assessment of a polygon database of soil organic carbon for greenhouse gas reporting in Canada’s Arctic and sub-arctic","year":2014,"lang":"en","type":"article","venue":"Information Processing in Agriculture","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Polygon (computer graphics); Arctic; Greenhouse gas; Environmental science; Soil carbon; The arctic; Database; Physical geography; Atmospheric sciences; Remote sensing; Geography; Soil science; Soil water; Geology; Computer science; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004764632,0.00009401194,0.0001976096,0.00008241772,0.00004065494,0.00002690858,0.00006014909,0.0000469484,0.00002251868],"category_scores_gemma":[0.000255225,0.00006664774,0.00001373444,0.0002645122,0.0000199791,0.000448385,0.00001001581,0.0001074195,1.355143e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004853953,"about_ca_system_score_gemma":0.0003622714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7733585,"about_ca_topic_score_gemma":0.9821697,"domain_scores_codex":[0.9987854,0.00002405671,0.0007395899,0.00009226886,0.0001940514,0.0001646655],"domain_scores_gemma":[0.9988597,0.00007069863,0.0008174704,0.00007559278,0.0001343943,0.00004213856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002326669,0.00001125394,0.983573,0.001758158,0.000002587946,0.000001284997,0.001604453,0.005728875,0.001018424,0.000005361528,0.0000317818,0.00624162],"study_design_scores_gemma":[0.0004205867,0.00005289206,0.891546,0.0005096307,0.00001108599,0.00002140651,0.001920457,0.1047447,0.0005479829,0.00004301729,0.00006197677,0.0001202412],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990373,0.0001129439,0.00003146219,0.0001436867,0.00005063217,0.000179451,0.0001969747,0.000005275364,0.0002423375],"genre_scores_gemma":[0.9984137,0.00004950451,0.000117364,0.00009502807,0.00001943902,0.000003307535,0.001297827,0.000001540015,0.000002321756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2088113,"threshold_uncertainty_score":0.2717816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695180082517184,"score_gpt":0.2332641923803545,"score_spread":0.2163123915551826,"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."}}