{"id":"W4247428166","doi":"10.1515/iupac.78.0542","title":"Riparian Zone","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Hydrology and Sediment Transport Processes","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Riparian zone; Relation (database); Pesticide; Computer science; Ecology; Biology; Data mining; Habitat","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":[0.0004136665,0.001185101,0.001117409,0.00370551,0.0005522336,0.001865461,0.001986879,0.0008713773,0.09046232],"category_scores_gemma":[0.002932767,0.0003728824,0.000751778,0.008677196,0.0002827707,0.00147806,0.001478653,0.001112679,0.09129428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212084,"about_ca_system_score_gemma":0.001791125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03438336,"about_ca_topic_score_gemma":0.06335504,"domain_scores_codex":[0.9993883,0.00006752062,0.0001030326,0.0002197726,0.0001372405,0.00008407349],"domain_scores_gemma":[0.9988052,0.0002575253,0.000231299,0.0002087712,0.0003880379,0.0001091859],"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":[0.00004876127,0.00001043395,0.001825903,0.001104699,0.0000261646,0.00004191247,0.00004875448,0.0002370734,0.0001297377,0.001057912,0.9911875,0.004281112],"study_design_scores_gemma":[0.00004292559,0.000005130849,0.005718319,0.000393442,0.00001434291,0.00005244592,0.0001082027,0.00011159,0.0001198526,0.0006388762,0.9927796,0.00001526601],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001047624,0.00006821823,0.00003615297,0.00002591851,0.000008699591,0.000005856419,0.998553,0.00007300331,0.001124448],"genre_scores_gemma":[0.0005194604,0.0001098335,0.0001840609,0.00003821872,0.000004221135,0.0000467717,0.9981477,0.00003414114,0.0009156228],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09046232,"threshold_uncertainty_score":0.3026266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008898670269420511,"score_gpt":0.3357669283075715,"score_spread":0.326868258038151,"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."}}