{"id":"W2010071533","doi":"10.1007/s10260-006-0031-7","title":"A weighted spatial median for clustered data","year":2006,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematics; Estimator; Multivariate statistics; Statistics; Affine transformation; Weighted median; Equivariant map; Spatial analysis; Invariant (physics); Spatial correlation; Spatial dependence; Sample (material); Applied mathematics; Combinatorics; Computer science; Artificial intelligence; Geometry; Pure mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001719894,0.0002832851,0.000532589,0.0000797638,0.000296488,0.00005539798,0.0006514619,0.0001506392,0.0002156949],"category_scores_gemma":[0.003637563,0.0002559756,0.00006537073,0.000242671,0.0002734836,0.0001061744,0.0002304257,0.0002277296,0.00002248026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005497769,"about_ca_system_score_gemma":0.00009228876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001192047,"about_ca_topic_score_gemma":0.0001440359,"domain_scores_codex":[0.9969473,0.0004641644,0.0008920587,0.0008755906,0.0002737529,0.000547104],"domain_scores_gemma":[0.9810413,0.01685958,0.0002236647,0.001409568,0.0002187478,0.0002471856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002654911,0.0001329357,0.000002314118,0.00006769328,0.00001716544,9.278767e-7,0.000010496,0.000001222402,0.0004137609,0.6274081,0.003489612,0.3684292],"study_design_scores_gemma":[0.0005386375,0.00004871656,0.00007629385,0.00001000067,0.0001714337,0.00000472008,0.00001800131,0.06477741,0.0002838601,0.8491163,0.08468556,0.0002690179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000004461809,0.00005596713,0.9868791,0.0006646926,0.0001231077,0.001931876,0.007474368,0.0001948566,0.002671632],"genre_scores_gemma":[0.001138953,0.000006393313,0.9948,0.0001214124,0.0005316427,0.001837686,0.001159426,0.00007621999,0.0003282319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3681602,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2244631394754292,"score_gpt":0.5450781388615388,"score_spread":0.3206149993861096,"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."}}