{"id":"W2508379722","doi":"10.1109/plasma.2016.7534208","title":"Application of laser induced breakdown spectroscopy (LIBS) for detection of lead contaminants in water using wood sample substrates","year":2016,"lang":"en","type":"article","venue":"","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Laser-induced breakdown spectroscopy; Detection limit; Contamination; Spectroscopy; Materials science; Laser; Analytical Chemistry (journal); Environmental science; Environmental chemistry; Chemistry; Optics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001458797,0.0001371934,0.0002359193,0.0001269072,0.00002198308,0.000006575629,0.00008525318,0.0001274532,0.00003097713],"category_scores_gemma":[0.0000191522,0.00009140708,0.00004977068,0.0001218225,0.00002070037,0.0001752894,0.000009507489,0.0000615294,0.00000770619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008243292,"about_ca_system_score_gemma":0.00001284241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004425323,"about_ca_topic_score_gemma":0.001235254,"domain_scores_codex":[0.9991373,0.00001481657,0.0003359772,0.0001637379,0.00008587216,0.0002622985],"domain_scores_gemma":[0.9995592,0.0001398868,0.00005052436,0.0001757197,0.00004637436,0.0000283144],"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":[0.00006179915,0.00003529884,0.001251984,0.0000690064,0.00001751769,1.713211e-7,0.0001051834,0.0002401886,0.9959026,0.00006565396,0.000001977415,0.002248633],"study_design_scores_gemma":[0.0008044032,0.0001125777,0.001659675,0.00003998004,0.00001411194,0.000001649591,0.0000476718,0.01050172,0.9861151,0.0005465066,0.00002299999,0.0001335809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8912717,0.00000393983,0.1080962,0.00001306578,0.00009798751,0.0003210687,0.00003263868,0.00005386166,0.0001095502],"genre_scores_gemma":[0.9976872,0.000007175419,0.002178228,0.000003583076,0.00003626889,0.00003616145,0.0000107448,0.0000255491,0.00001508805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1064156,"threshold_uncertainty_score":0.3727473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621103445427448,"score_gpt":0.2441699410870972,"score_spread":0.2279589066328227,"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."}}