{"id":"W1977392436","doi":"10.1139/f02-112","title":"Least median of squares: a suitable objective function for stock assessment models?","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Southeast Fisheries Science Center; National Marine Fisheries Service","keywords":"Outlier; Statistics; Computer science; Least-squares function approximation; Econometrics; Function (biology); Least trimmed squares; Data mining; Estimation theory; Mathematics; Mathematical optimization; Non-linear least squares; 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":[0.006635745,0.001377662,0.001818277,0.001254669,0.0005118435,0.001940097,0.001418636,0.003758301,0.002834804],"category_scores_gemma":[0.01714783,0.0006955598,0.001000959,0.00200208,0.001199806,0.003856712,0.001483181,0.002515052,0.002132571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000616658,"about_ca_system_score_gemma":0.001110804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001664129,"about_ca_topic_score_gemma":0.001801908,"domain_scores_codex":[0.9976507,0.001458601,0.0001210004,0.0003374078,0.0003434305,0.00008892772],"domain_scores_gemma":[0.9973602,0.001601905,0.0002603222,0.0002339389,0.0004665151,0.00007702591],"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":[0.0002370101,0.0001112666,0.002954168,0.0008292165,0.0002573152,0.0002133964,0.0002519336,0.3137252,0.005607385,0.1646844,0.02535501,0.4857737],"study_design_scores_gemma":[0.00002559877,0.0000683037,0.0007307414,0.0001267327,0.00003338642,0.000128145,0.00007932516,0.8430539,0.002173962,0.13286,0.02065526,0.00006457084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001267186,0.0006851618,0.9963197,0.0008607954,0.00008894112,0.00001372639,0.00009546568,0.0001797697,0.0004893579],"genre_scores_gemma":[0.1010964,0.002546009,0.8873862,0.0008674809,0.0004706527,0.000311117,0.000582822,0.001077448,0.005661803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006635745,"threshold_uncertainty_score":0.03509355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1707873729938232,"score_gpt":0.3657382066545844,"score_spread":0.1949508336607612,"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."}}