{"id":"W1974414650","doi":"10.1016/j.chroma.2005.01.041","title":"Characterization and pattern recognition of oil–sand naphthenic acids using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry","year":2005,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Environment and Climate Change Canada; Ministry of the Environment, Conservation and Parks","funders":"Syncrude; University of Saskatchewan","keywords":"Naphthenic acid; Chemistry; Mass spectrometry; Chromatography; Extraction (chemistry); Gas chromatography; Oil sands; Time-of-flight mass spectrometry; Mass spectrum; Gas chromatography–mass spectrometry; Two-dimensional gas; Homologous series; Characterization (materials science); Ion; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002134192,0.0002858803,0.0007822523,0.001096243,0.0001091877,0.00005025428,0.0001930928,0.0001472378,0.0003782102],"category_scores_gemma":[0.00001875203,0.0002582609,0.0005709633,0.0008631673,0.0002172004,0.0004278659,0.00003464173,0.0002785411,0.000001992573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003132304,"about_ca_system_score_gemma":0.00007450495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009621574,"about_ca_topic_score_gemma":6.634363e-7,"domain_scores_codex":[0.9977599,0.00006535937,0.001071912,0.0002424396,0.000592749,0.0002676778],"domain_scores_gemma":[0.9972607,0.00008979715,0.001795689,0.0002026438,0.0004971026,0.0001540879],"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":[0.00009980004,0.000208082,0.01594187,0.0003547843,0.0006796604,0.0000167513,0.0001637589,0.00005312735,0.9675835,7.151449e-7,0.00001112932,0.0148868],"study_design_scores_gemma":[0.002941378,0.0001674723,0.005796947,0.002038189,0.0008524975,0.0009048316,0.0001621736,0.004262851,0.9820929,0.0001127226,0.0002344011,0.0004336326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966761,0.001818586,0.0009559921,0.00007247296,0.00004764154,0.00001038903,0.00007475996,0.00002174512,0.0003222629],"genre_scores_gemma":[0.9933093,0.0003528663,0.005914798,0.00004247451,0.0002770155,8.591664e-7,0.00004783316,0.00003177089,0.00002304251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01450938,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208809580638381,"score_gpt":0.2378528390826845,"score_spread":0.2257647432763006,"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."}}