{"id":"W7027202305","doi":"","title":"Canadian Sub - Group 9 pollution from agricultural, forestry and conservation sources","year":2018,"lang":"en","type":"report","venue":"The Atrium (University of Guelph)","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pollution; Pollution prevention; Agriculture; Order (exchange); Air pollution; Land use","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009653501,0.0008167859,0.0004413287,0.00390382,0.007536609,0.001859785,0.001199423,0.0008319328,0.01497231],"category_scores_gemma":[0.001100159,0.0003647149,0.0008956852,0.005383505,0.0006175905,0.000502156,0.002279851,0.0008415999,0.002039585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02007408,"about_ca_system_score_gemma":0.08424558,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9700102,"about_ca_topic_score_gemma":0.9869896,"domain_scores_codex":[0.9953003,0.00009936289,0.0001373787,0.0002485979,0.003048172,0.001166172],"domain_scores_gemma":[0.9968987,0.0001096442,0.0001673026,0.00009507447,0.002167655,0.0005617217],"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":[0.001046599,0.0005827819,0.2446947,0.001760173,0.0002608553,0.00374455,0.008830247,0.002036957,0.02581422,0.01557568,0.4711424,0.2245109],"study_design_scores_gemma":[0.00003462788,0.000152416,0.3103928,0.0001309073,0.00007025363,0.0003746524,0.004428247,0.0004203296,0.007626662,0.0005045682,0.6758031,0.00006139739],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2640962,0.004407117,0.002631945,0.007555398,0.0004441007,0.004862982,0.09644761,0.0004484981,0.6191062],"genre_scores_gemma":[0.3764609,0.008431514,0.008855203,0.003922037,0.0001431503,0.001193296,0.08027972,0.0001656677,0.5205485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02998984,"threshold_uncertainty_score":0.1456483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187562438221053,"score_gpt":0.1795977037836691,"score_spread":0.1677220794014586,"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."}}