{"id":"W1527406032","doi":"10.5539/eer.v5n1p75","title":"Using Simulation to Test the Reliability of Regression Models","year":2015,"lang":"en","type":"article","venue":"Energy and Environment Research","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems; National Science Foundation","keywords":"Reliability (semiconductor); Computer science; Range (aeronautics); Regression analysis; Sample size determination; Linear regression; Statistics; Regression; Statistical model; Mathematics; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003178314,0.00007474345,0.0001185667,0.0001238558,0.0002140099,0.00005855998,0.0005126289,0.00005725923,0.000009625342],"category_scores_gemma":[0.0006141488,0.00004597283,0.00003495155,0.0004573151,0.0001859587,0.0002633868,0.0008316206,0.000148031,0.000005850347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071021,"about_ca_system_score_gemma":0.00006159589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004609045,"about_ca_topic_score_gemma":0.000009345894,"domain_scores_codex":[0.9977145,0.000433887,0.0001848027,0.0003527222,0.001044546,0.0002695878],"domain_scores_gemma":[0.9979129,0.00103778,0.00002866456,0.0007240384,0.0001030208,0.0001935874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001664687,0.00013433,0.00175899,0.000006886958,0.000004128304,0.00000130841,0.000439384,0.9757305,0.0006437095,0.003245056,0.0001027148,0.01791638],"study_design_scores_gemma":[0.0001018105,0.0001435556,0.000544126,0.00001430262,0.000001744741,7.080847e-7,0.00006957879,0.9644029,0.001688002,0.02881631,0.004160533,0.00005649122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2801248,0.0003161515,0.7165073,0.002284788,0.00002111362,0.0001380664,0.000001284366,0.00001856955,0.0005879618],"genre_scores_gemma":[0.9925084,0.0001389749,0.007053752,0.0000272225,0.00002584945,0.00001139816,9.126246e-7,0.000004175005,0.0002293008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7123836,"threshold_uncertainty_score":0.1874718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1608722287389324,"score_gpt":0.376096487047994,"score_spread":0.2152242583090615,"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."}}