{"id":"W7001525429","doi":"","title":"2018 Kentucky River Watershed Watch: Annual Report","year":2019,"lang":"en","type":"article","venue":"UKnowledge (University of Kentucky)","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Watershed Watch Salmon Society","keywords":"Annual report; Watershed; Hydrology (agriculture); Water resources; Drainage basin","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001250233,0.0006849461,0.0005772254,0.002930688,0.001234228,0.003026121,0.0009307593,0.001752287,0.05940162],"category_scores_gemma":[0.003957888,0.0004225101,0.0003754417,0.003795355,0.0003784118,0.001835086,0.00253969,0.001210696,0.0232806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004185903,"about_ca_system_score_gemma":0.01779329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4005308,"about_ca_topic_score_gemma":0.5686181,"domain_scores_codex":[0.9992309,0.00003498036,0.0001023314,0.0001005728,0.0004046518,0.0001266213],"domain_scores_gemma":[0.9962184,0.00018165,0.0004269159,0.0001514613,0.002399533,0.0006220577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008473128,0.00001977006,0.005457602,0.0001481649,0.00000733809,0.00005703179,0.00005556098,0.00009371897,0.0001728004,0.0002126665,0.9654993,0.02819136],"study_design_scores_gemma":[0.00001712048,0.000009219137,0.03430577,0.0003051451,0.00001362019,0.00001851855,0.0003064394,0.0001217925,0.0002492537,0.0002323442,0.9644052,0.00001570256],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.02510156,0.01271655,0.001786195,0.03520163,0.01420168,0.000807844,0.7266067,0.002083896,0.1814939],"genre_scores_gemma":[0.04576017,0.01338561,0.00353425,0.005108285,0.001426049,0.001525409,0.4056199,0.0009934924,0.5226468],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4005308,"threshold_uncertainty_score":0.7963988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005168307000851772,"score_gpt":0.1673091917591305,"score_spread":0.1621408847582787,"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."}}