{"id":"W4401454961","doi":"10.2139/ssrn.4921091","title":"Low-Limit Salinity Monitoring in Drinking Water Using a Electrochemical Microfluidic Sensor with Ion-Selective Polymer Membrane","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Microfluidics; Membrane; Detection limit; Electrochemical gas sensor; Limit (mathematics); Ion; Electrochemistry; Salinity; Materials science; Polymer; Optoelectronics; Nanotechnology; Environmental science; Chemistry; Chromatography; Electrode; Composite material; Ecology; Biology","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":[0.0004310411,0.0004136307,0.0004717062,0.0002714503,0.0002303154,0.000536154,0.0006831429,0.0008216634,0.0004970033],"category_scores_gemma":[0.000585324,0.0003069803,0.0002585055,0.0001961914,0.0003208111,0.0005975128,0.0006503787,0.0003827641,0.0004145245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003980804,"about_ca_system_score_gemma":0.0003923156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002685614,"about_ca_topic_score_gemma":0.0003331428,"domain_scores_codex":[0.9995128,0.0000528985,0.00002667502,0.0001783609,0.0001822752,0.00004697281],"domain_scores_gemma":[0.9998023,0.00006734552,0.00003950941,0.00001824331,0.00005205284,0.00002055015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006787221,0.00001439641,0.0004593015,0.00003761817,0.000004976097,0.00001869735,0.00001264809,0.00007342026,0.995176,0.0001166841,0.00007764182,0.003940791],"study_design_scores_gemma":[0.000008180066,0.0001099645,0.0009255596,0.000003541997,0.00001194618,0.0001014728,0.000008976278,0.003099922,0.9942656,0.00007956271,0.001375673,0.000009596814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8793404,0.00384008,0.1105966,0.0007762382,0.000424212,0.0001144079,0.000639274,0.0009766505,0.003292134],"genre_scores_gemma":[0.9570496,0.0009770263,0.0389801,0.0002845814,0.0001114053,0.0000835698,0.0001710713,0.00002697763,0.002315714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008216634,"threshold_uncertainty_score":0.002888322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106400928312916,"score_gpt":0.2401769462702247,"score_spread":0.2295368534389331,"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."}}