{"id":"W3077712470","doi":"10.1007/s11368-020-02755-4","title":"Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes","year":2020,"lang":"en","type":"article","venue":"Journal of Soils and Sediments","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":218,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Environment and Protected Areas; University of Waterloo","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences; Fonds National de la Recherche Luxembourg; UK Research and Innovation","keywords":"Data science; Benchmarking; Scope (computer science); Computer science; Sampling (signal processing); Environmental resource management; Environmental science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004146505,0.0001547991,0.0002762944,0.0000183733,0.0001668461,0.0000547734,0.0001189046,0.0000645844,0.00009142125],"category_scores_gemma":[0.00003239563,0.00006638152,0.00007224629,0.0000871335,0.00003578098,0.0001501637,0.00006773585,0.0001935767,0.000001345319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001173769,"about_ca_system_score_gemma":0.000007657906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007638466,"about_ca_topic_score_gemma":0.000003649148,"domain_scores_codex":[0.9987398,0.00004489796,0.0004125699,0.0002146259,0.0003612344,0.0002268499],"domain_scores_gemma":[0.9992709,0.00009249425,0.0002702281,0.00002606157,0.00006987829,0.0002704433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001046522,0.00008342954,0.01623615,0.00003940627,0.00008304389,0.00002514745,0.005560466,0.00001468877,0.03279451,0.0000994334,0.000349607,0.9446095],"study_design_scores_gemma":[0.003869307,0.004235407,0.4490864,0.001214946,0.0002966596,0.0001765173,0.03016325,0.0020769,0.01479457,0.0006422595,0.4921697,0.001274131],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765301,0.01065722,0.00001559929,0.01203085,0.0001974796,0.00007286831,0.00000133312,0.00001890905,0.0004756538],"genre_scores_gemma":[0.9814535,0.01639756,0.0002554021,0.001348612,0.0005172217,0.000001063634,0.000002491361,0.000001985294,0.00002220215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9433354,"threshold_uncertainty_score":0.270696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04864692489473949,"score_gpt":0.232346992933048,"score_spread":0.1837000680383085,"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."}}