{"id":"W2090580703","doi":"10.1016/s0021-9673(02)01003-8","title":"Characterization and source identification of hydrocarbons in water samples using multiple analytical techniques","year":2002,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"Golder Associates (Canada); Environment and Climate Change Canada","funders":"","keywords":"BTEX; Chemistry; Aquifer; Gasoline; Environmental chemistry; Petroleum product; Ethylbenzene; Petroleum; Flame ionization detector; Hydrocarbon; Contamination; Gas chromatography; Groundwater; Toluene; Pollutant; Chromatography; Gas chromatography–mass spectrometry; Solid-phase microextraction; Mass spectrometry; Geology; Organic chemistry","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":[0.0002761655,0.000523603,0.000301551,0.0007611772,0.0004019541,0.0004585694,0.0002982569,0.000507166,0.0006381187],"category_scores_gemma":[0.000764559,0.0002220746,0.0002601569,0.0004436976,0.0003026795,0.0006199167,0.0003986535,0.0005274475,0.0003531698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002269101,"about_ca_system_score_gemma":0.0004174268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608182,"about_ca_topic_score_gemma":0.002330975,"domain_scores_codex":[0.999671,0.00003569634,0.00002503745,0.00008780532,0.0001277309,0.00005274852],"domain_scores_gemma":[0.9997162,0.00008499713,0.00004094457,0.000017311,0.0001101662,0.00003033919],"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":[0.00008190902,0.00001624992,0.0009422902,0.00001738991,0.000004326372,0.00002402721,0.00002414244,0.0000370053,0.9953714,0.00001702324,0.00001344428,0.003450849],"study_design_scores_gemma":[0.000004770822,0.00009876124,0.00417649,0.000002855558,0.00001561171,0.0001358107,0.0000437394,0.0005742035,0.9944344,0.00003685422,0.0004701047,0.000006323315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734605,0.0007992761,0.02376394,0.0001065217,0.00002115137,0.00006959177,0.0006156978,0.0001061511,0.00105715],"genre_scores_gemma":[0.9642355,0.001180105,0.03008903,0.00008966156,0.00002177895,0.000112637,0.001023407,0.00006307216,0.003184961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001608182,"threshold_uncertainty_score":0.00319767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01631182601293848,"score_gpt":0.2189575499071358,"score_spread":0.2026457238941973,"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."}}