{"id":"W2080810142","doi":"10.2118/01-07-02","title":"A New Method for Group Analysis of Petroleum Fractions in Unconsolidated Porous Media","year":2001,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Petroleum; Porosity; Asphalt; Characterization (materials science); Extraction (chemistry); Porous medium; Petroleum engineering; Well logging; Hydrocarbon mixtures; Fraction (chemistry); Proton NMR; Hydrocarbon; Logging; Chemistry; Chromatography; Materials science; Geology; Organic chemistry; Nanotechnology; Composite material","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":[0.000263839,0.0001418544,0.0005825433,0.00622351,0.00007289442,0.00001645804,0.0003531313,0.0001363996,0.0004366898],"category_scores_gemma":[0.000034235,0.0001434561,0.0002537789,0.003350952,0.00004888941,0.0001104575,0.00001448831,0.0004059119,0.000002974777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157556,"about_ca_system_score_gemma":0.0006029007,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07395381,"about_ca_topic_score_gemma":0.2803037,"domain_scores_codex":[0.9986585,0.00002991331,0.0006411991,0.000169657,0.0001223494,0.0003784267],"domain_scores_gemma":[0.9986634,0.00014378,0.0005242098,0.0002740889,0.0001382819,0.0002562724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002264385,0.0005542588,0.4135397,0.00001171398,0.004832584,0.00008232832,0.0003377316,0.006841118,0.0260764,0.4786525,0.01033409,0.0585111],"study_design_scores_gemma":[0.008258767,0.001240982,0.08575502,0.0001235597,0.005942989,0.0002630444,0.006614004,0.01592191,0.01295978,0.1672041,0.6944916,0.00122424],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4855754,0.0003030541,0.4943898,0.01392125,0.000231598,0.0001959076,0.0002867575,0.00002841488,0.005067735],"genre_scores_gemma":[0.9651347,0.00002323065,0.03436096,0.00005682481,0.0001484263,0.00002052366,0.00002970016,0.00001562076,0.0002100022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6841575,"threshold_uncertainty_score":0.9322128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009269906137199663,"score_gpt":0.3166007733211487,"score_spread":0.3073308671839491,"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."}}