{"id":"W1996089137","doi":"10.5539/ijc.v4n2p26","title":"Applying the Inverse Gaussian Distribution to the Assessment of Chemical Reactor Performance","year":2012,"lang":"en","type":"article","venue":"International Journal of Chemistry","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Inverse Gaussian distribution; Reliability (semiconductor); Chemistry; Gaussian; Inverse; Chemical reactor; Scale (ratio); Reliability engineering; Applied mathematics; Statistical physics; Biochemical engineering; Distribution (mathematics); Chemical engineering; Thermodynamics; Computational chemistry; Engineering; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002326575,0.00007070962,0.000147323,0.00003208401,0.00004078229,0.00006710478,0.001249625,0.00003931921,0.0002539102],"category_scores_gemma":[0.000901504,0.00003374186,0.0001894712,0.000226558,0.00007057004,0.0002599755,0.0001241619,0.0002351944,0.00001467784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110276,"about_ca_system_score_gemma":0.00008461485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004108605,"about_ca_topic_score_gemma":7.360882e-7,"domain_scores_codex":[0.9974384,0.00004424263,0.0006215259,0.0000795322,0.001697973,0.000118376],"domain_scores_gemma":[0.9980619,0.0003731033,0.000610558,0.0002267758,0.0006321252,0.00009556186],"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":[0.0003211322,0.0004740384,0.2492574,0.00001494114,0.0006945191,0.00001115706,0.00165851,0.002274257,0.4838561,0.0005532611,0.0586671,0.2022175],"study_design_scores_gemma":[0.0006914317,0.0000367358,0.08788899,0.000121752,0.0001301961,0.0003418443,0.002573492,0.00652892,0.5413653,0.001259526,0.3588256,0.000236213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845972,0.00005051758,0.00432856,0.008356151,0.0004055213,0.00005678607,0.00002318622,0.000002093622,0.002179991],"genre_scores_gemma":[0.9986462,0.00004541071,0.0003417521,0.0001470466,0.000679614,0.00000742241,0.000005928518,0.000002770878,0.0001238705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3001585,"threshold_uncertainty_score":0.2780138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04563338942827222,"score_gpt":0.3784606403779177,"score_spread":0.3328272509496454,"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."}}