{"id":"W2340866568","doi":"10.1149/ma2016-01/39/2002","title":"Qualification and Quantification Analyses of Iron (III) Ions in a Variety of Samples, Using Nano-Structured Metal-Oxide Based Electrochemical Sensors","year":2016,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Electrochemistry; Metal ions in aqueous solution; Iron oxide; Detection limit; Nanotechnology; Electrochemical gas sensor; Oxide; Seawater; Materials science; Metal; Ion; Electrode; Chemistry; Environmental chemistry; Metallurgy; Chromatography","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":[0.0004444999,0.0004432491,0.0004018869,0.0003818175,0.0001829396,0.0003415989,0.0006787424,0.001020711,0.0003092398],"category_scores_gemma":[0.0006063464,0.0002352829,0.0003308519,0.0001853878,0.0002660631,0.0005400108,0.0002874254,0.0003937028,0.0003329767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000299923,"about_ca_system_score_gemma":0.0002040256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003645254,"about_ca_topic_score_gemma":0.0009795816,"domain_scores_codex":[0.9995458,0.00004340542,0.00003826755,0.0001232658,0.0002240757,0.00002520147],"domain_scores_gemma":[0.9997787,0.00004595033,0.00004742989,0.00001999042,0.00009508453,0.00001286503],"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":[0.0000178061,0.00002052301,0.0002358814,0.0001001639,0.000005044199,0.00003996558,0.00001386758,0.0001756253,0.9940837,0.00006292776,0.00005320925,0.005191271],"study_design_scores_gemma":[0.000003608199,0.0001175198,0.0008221999,0.00000812091,0.00001209445,0.0001860286,0.00001352157,0.003545949,0.9938062,0.00004056736,0.001433716,0.00001051978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7090479,0.0112447,0.2726583,0.0006860199,0.0003967026,0.0004535405,0.0007551658,0.0009898315,0.003767973],"genre_scores_gemma":[0.7000482,0.004489745,0.2896364,0.0004423457,0.00007429146,0.0002617404,0.0004860196,0.00004561856,0.004515619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001020711,"threshold_uncertainty_score":0.002350807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04064801604505534,"score_gpt":0.3018612052320865,"score_spread":0.2612131891870311,"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."}}