{"id":"W3017016299","doi":"10.1016/j.aeaoa.2020.100072","title":"Mapping the deposition of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"> <mml:mrow> <mml:mmultiscripts> <mml:mtext>C</mml:mtext> <mml:mprescripts/> <mml:none/> <mml:mn>137</mml:mn> </mml:mmultiscripts> <mml:mtext>s</mml:mtext> </mml:mrow> </mml:math> and <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si2.svg\"> <mml:mrow> <mml:mmultiscripts> <mml:mtext>I</mml:mtext> <mml:mprescripts/> <mml:none/> <mml:mn>131</mml:mn> </mml:mmultiscripts> </mml:mrow> </mml:math> in North America following the 2011 Fukushima Daiichi Reactor accident","year":2020,"lang":"lv","type":"article","venue":"Atmospheric Environment X","topic":"Radioactive contamination and transfer","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Health Canada","funders":"","keywords":"Context (archaeology); Deposition (geology); Nuclear explosion; Algorithm; Computer science; Environmental science; Meteorology; Physics; Geology; Nuclear physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002160075,0.000951542,0.0006178336,0.003407258,0.0008551137,0.002779066,0.001231855,0.001413409,0.08618417],"category_scores_gemma":[0.006835961,0.001162377,0.001235976,0.005322796,0.0004746565,0.002375452,0.001889835,0.001975814,0.07328288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001982914,"about_ca_system_score_gemma":0.004272606,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05043009,"about_ca_topic_score_gemma":0.06465065,"domain_scores_codex":[0.9986556,0.0001670221,0.0001686417,0.0003645585,0.0005082891,0.0001357494],"domain_scores_gemma":[0.9964764,0.001069015,0.0003058026,0.0009327196,0.001031219,0.0001849523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004942808,0.0001860297,0.009986518,0.00154459,0.000103452,0.0004663502,0.001668183,0.00407983,0.01975518,0.01806016,0.812401,0.1312545],"study_design_scores_gemma":[0.00005570682,0.00002509926,0.00847727,0.0001371658,0.00004021422,0.00008523944,0.0002878953,0.002792146,0.01621691,0.003775052,0.9680346,0.00007262741],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01852706,0.0006267612,0.08635835,0.002080073,0.001118085,0.0005700242,0.6645566,0.05260816,0.1735549],"genre_scores_gemma":[0.06649984,0.001751625,0.1240104,0.001093167,0.0002332449,0.001282618,0.6622719,0.04630026,0.09655678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9495699,"threshold_uncertainty_score":0.2883148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972513670243166,"score_gpt":0.2224113926925449,"score_spread":0.2026862559901133,"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."}}