{"id":"W2892004684","doi":"10.5539/ijsp.v7n6p33","title":"Heteroscedasticity and Model Selection via Partitioning in Fisheries Data","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heteroscedasticity; Ordinary least squares; Generalized least squares; Selection (genetic algorithm); Model selection; Statistics; Mathematics; Set (abstract data type); Data set; Least-squares function approximation; Econometrics; Computer science; Data mining; Mathematical optimization; Artificial intelligence; Estimator","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03252081,0.001552455,0.001735185,0.00291175,0.001665724,0.002899422,0.002089513,0.001193719,0.001006997],"category_scores_gemma":[0.07240282,0.0008342103,0.002761256,0.003543633,0.001873755,0.002007351,0.002704325,0.002463935,0.0003368075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00167425,"about_ca_system_score_gemma":0.00277685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007884301,"about_ca_topic_score_gemma":0.009027891,"domain_scores_codex":[0.9705076,0.02132376,0.001742668,0.003595363,0.002402743,0.0004277924],"domain_scores_gemma":[0.9494951,0.04204325,0.002465185,0.003874357,0.001915068,0.0002070664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005923454,0.0002958304,0.1168986,0.001666114,0.003139235,0.001937048,0.004618713,0.3772053,0.007536522,0.07255919,0.005046684,0.4085044],"study_design_scores_gemma":[0.0000875843,0.0003307008,0.03422527,0.0004427072,0.0003876698,0.0004490587,0.001512349,0.8050084,0.00582165,0.1418364,0.009663582,0.0002346203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09437172,0.000820522,0.901675,0.0006545773,0.00008151704,0.0004728071,0.0005697538,0.0004134072,0.0009406434],"genre_scores_gemma":[0.5358135,0.0005641764,0.4591438,0.0003171456,0.00008412093,0.001210109,0.00196128,0.0002417636,0.000664197],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03252081,"threshold_uncertainty_score":0.1719885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1598710399318501,"score_gpt":0.4142415229137705,"score_spread":0.2543704829819204,"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."}}