{"id":"W4302983150","doi":"10.13179/canchemtrans.2014.02.03.0113","title":"Utilization of 2-Nitro-6-(thiazol-2-yldiazenyl)phenol for Spectrophotometric Determination of Trace Amounts of Copper(II) in Water Samples","year":2014,"lang":"en","type":"article","venue":"Canadian Chemical Transactions","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"TRACE (psycholinguistics); Copper; Phenol; Chemistry; Nitro; Trace Amounts; Chromatography; Nuclear chemistry; Organic chemistry; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001953139,0.00008582503,0.0002137046,0.0002656087,0.00004785818,0.000004077476,0.0001281288,0.000086011,0.0002478268],"category_scores_gemma":[0.00004978724,0.00007968597,0.00009670653,0.0004921873,0.0001200634,0.00008229838,0.000003914109,0.00006606638,0.000002643282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001993841,"about_ca_system_score_gemma":0.00001989331,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04144836,"about_ca_topic_score_gemma":0.008013282,"domain_scores_codex":[0.9990959,0.00003038987,0.0003368884,0.0001710198,0.0001554456,0.0002103859],"domain_scores_gemma":[0.9995683,0.00007787091,0.00006419665,0.0001462756,0.00002770132,0.0001156289],"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":[0.00001195492,0.0000903403,0.003659401,0.00006538725,0.00001114549,1.218074e-7,0.0007393842,0.0005252218,0.9884235,0.00001535234,0.00001440623,0.006443792],"study_design_scores_gemma":[0.0002709805,0.00004499009,0.003439752,0.00001795238,0.00004167099,5.849209e-7,0.00008441868,0.001920318,0.9933174,0.0002838296,0.0004896359,0.00008845005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983898,0.000008765373,0.01557572,0.0001021842,0.0000381676,0.0001320248,0.00007213424,0.000005625713,0.000167326],"genre_scores_gemma":[0.9976424,0.000006698072,0.002211174,0.000006635183,0.00001408161,0.00001651287,0.00002445237,0.000009436617,0.00006862315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03343508,"threshold_uncertainty_score":0.9649347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147067818401179,"score_gpt":0.2546029406034608,"score_spread":0.223132262419449,"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."}}