{"id":"W1996991143","doi":"10.1021/ef050097g","title":"Derivation of Molecular Representations of Middle Distillates","year":2005,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Process Optimization and Integration","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hydrocarbon mixtures; Chemistry; Distillation; Representation (politics); Hydrocarbon; Combining rules; Homologous series; Molecule; Mixing (physics); Biological system; Consistency (knowledge bases); Molecular descriptor; Thermodynamics; Series (stratigraphy); Mass spectrometry; Statistical physics; Quantitative structure–activity relationship; Organic chemistry; Mathematics; Chromatography; Physics","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":[0.00001985621,0.00004761862,0.00007140846,0.00005861199,0.00001003844,0.000004483517,0.00004641528,0.00002991854,0.00009459209],"category_scores_gemma":[0.00002831852,0.00004859053,0.00002654562,0.0001418514,0.00001541067,0.0001189383,0.000005306315,0.00001849044,0.0000018545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001083539,"about_ca_system_score_gemma":0.000006520625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001830121,"about_ca_topic_score_gemma":0.00001513842,"domain_scores_codex":[0.9996417,0.000006328406,0.0001772542,0.00005332363,0.00007147367,0.00004994194],"domain_scores_gemma":[0.9997653,0.00001227205,0.00003870731,0.00009570872,0.0000717226,0.0000163376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002496721,0.00001919415,0.0001207274,0.00002750476,0.00002108355,1.280775e-7,0.0002160068,0.5185633,0.4503163,0.02189763,0.0002160847,0.008599597],"study_design_scores_gemma":[0.00008195149,0.000008322015,0.0002457796,0.00001413272,0.000005677397,3.692804e-7,0.00003020149,0.04343072,0.9539266,0.0004816664,0.001728418,0.00004617402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5070984,0.0008112992,0.4637239,0.00009419912,0.00006917524,0.00003680629,0.000008218452,0.00009610176,0.02806184],"genre_scores_gemma":[0.9936928,0.0001098298,0.005909096,0.00002250853,0.00001881005,0.000006599548,0.00004022608,0.000009326236,0.0001908613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5036103,"threshold_uncertainty_score":0.1981464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009104778908827282,"score_gpt":0.2166627181503276,"score_spread":0.2075579392415003,"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."}}