{"id":"W3123160680","doi":"10.20944/preprints201904.0002.v1","title":"Colorimetric Detection of Mercury Ions in Water with Capped Silver Nanoprisms","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Centres of Excellence","keywords":"Mercury (programming language); Detection limit; Reagent; Chemistry; Ion; Nanoparticle; Metal ions in aqueous solution; Galvanic cell; Silver nanoparticle; Nanotechnology; Inorganic chemistry; Materials science; Chromatography; Organic chemistry; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00049311,0.0009222131,0.0004458028,0.0006322333,0.0002403681,0.0004298147,0.001197836,0.0009179483,0.001918037],"category_scores_gemma":[0.0009341505,0.0004689861,0.0003151054,0.0003779323,0.0003716135,0.0005698169,0.0006227719,0.0008865501,0.001560417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004441985,"about_ca_system_score_gemma":0.0001622559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003616154,"about_ca_topic_score_gemma":0.0008587758,"domain_scores_codex":[0.9993315,0.00011874,0.00003847394,0.0001680934,0.0002753813,0.00006784654],"domain_scores_gemma":[0.9996138,0.0001416272,0.00005839164,0.00004536993,0.0001016955,0.00003912942],"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":[0.00001551745,0.000006228432,0.00003272759,0.00003690321,0.000003613772,0.00002414,0.00001391423,0.00006409114,0.9984443,0.00005330329,0.00008685427,0.001218455],"study_design_scores_gemma":[0.000003185677,0.00003869305,0.0001756363,0.000002837151,0.000004466521,0.0000412283,0.000007491953,0.001083495,0.9976839,0.0000631143,0.000890452,0.000005340911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.693288,0.0050465,0.2809409,0.0006512471,0.0007069452,0.0003954055,0.001344441,0.004989212,0.01263737],"genre_scores_gemma":[0.6906224,0.002265838,0.2860149,0.0005524317,0.0001270154,0.0003357667,0.001584856,0.0004967992,0.01799996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001918037,"threshold_uncertainty_score":0.0064165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330064257542725,"score_gpt":0.3047315100259181,"score_spread":0.2717250842716456,"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."}}