{"id":"W4404305443","doi":"10.1016/j.fuel.2024.133732","title":"Molecular characterization of condensates altered by thermochemical sulfate reduction and evaporative fractionation using high-resolution mass spectrometry","year":2024,"lang":"en","type":"article","venue":"Fuel","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Natural Science Foundation of Xinjiang; China Scholarship Council; Natural Science Foundation of Xinjiang Province","keywords":"Mass spectrometry; Fractionation; Chemistry; Characterization (materials science); Sulfate; Resolution (logic); Chromatography; High resolution; Analytical Chemistry (journal); Materials science; Organic chemistry; Nanotechnology","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":[0.00007288203,0.00009447256,0.0001180538,0.0001782145,0.00002599915,0.000042764,0.00002892449,0.00007929167,0.00004986229],"category_scores_gemma":[0.00001066808,0.00009384571,0.00003458497,0.0003532591,0.00003220979,0.0002782352,0.000006647027,0.00009758262,0.000004971977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000772516,"about_ca_system_score_gemma":0.00001047756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001206159,"about_ca_topic_score_gemma":3.809665e-7,"domain_scores_codex":[0.9994042,0.00003142423,0.0001862663,0.0001446308,0.0001482383,0.00008522928],"domain_scores_gemma":[0.999794,0.00001091623,0.00003882743,0.00007361499,0.00005050532,0.00003211154],"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":[0.000006141106,0.000007677715,0.00001042081,0.00007462665,0.00008645709,9.597331e-7,0.0001491377,0.004951745,0.9943611,0.0001769059,0.00003148018,0.0001433292],"study_design_scores_gemma":[0.00008141308,0.00001070108,0.0001741847,0.00002402395,0.00003367043,0.000003260511,0.00005001145,0.4330291,0.5658704,0.000603815,0.00004185727,0.00007758458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9118269,0.0006358914,0.08688037,0.0001240759,0.0002220226,0.00007781715,0.00002251813,0.0001102437,0.0001001807],"genre_scores_gemma":[0.9988182,0.0001915934,0.0004713364,0.000004355573,0.00008361851,0.000006078296,0.0003733118,0.00001978431,0.00003177963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4284907,"threshold_uncertainty_score":0.3826917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009335492865103125,"score_gpt":0.226036731718547,"score_spread":0.2167012388534439,"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."}}