{"id":"W2016247510","doi":"10.1179/003962608x389988","title":"Comparison and Analysis of Non-Linear Least Squares Methods for 3-D Coordinates Transformation","year":2009,"lang":"en","type":"article","venue":"Survey Review","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Curvilinear coordinates; Geodetic datum; Coordinate descent; Transformation (genetics); Mathematics; Levenberg–Marquardt algorithm; Least-squares function approximation; Set (abstract data type); Coordinate system; Applied mathematics; Mathematical optimization; Algorithm; Geodesy; Computer science; Geography; Statistics; Geometry; Artificial neural network; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.002025112,0.0001068216,0.000927236,0.0000578387,0.00003481262,0.000007031448,0.00007125449,0.00003385497,0.00005231178],"category_scores_gemma":[0.001334595,0.00007624177,0.0001479459,0.0006281015,0.00003478803,0.00004483916,0.000006008951,0.00005267303,0.000001530452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006381007,"about_ca_system_score_gemma":0.000007109423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003066261,"about_ca_topic_score_gemma":0.00001127765,"domain_scores_codex":[0.9987783,0.0003330166,0.00054857,0.0001286582,0.00009242582,0.0001190289],"domain_scores_gemma":[0.9973294,0.002178561,0.0001529408,0.0001237024,0.0001588999,0.00005646881],"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":[0.0000298812,0.0001522352,0.001491342,0.002963303,0.0002606896,9.660106e-8,0.0001027567,0.000002646327,0.00003648409,0.003819732,0.0007191561,0.9904217],"study_design_scores_gemma":[0.001388098,0.0015517,0.426919,0.002804074,0.009867284,0.000003272806,0.00006854247,0.4769008,0.001432649,0.06349511,0.01462833,0.0009412192],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001116503,0.01596157,0.9818872,0.0002566239,0.00001903712,0.0004362472,0.0001729244,0.00001528886,0.0001346356],"genre_scores_gemma":[0.08449912,0.01213768,0.902462,0.0003391601,0.0000204284,0.00004439599,0.0004137521,0.00001589038,0.00006756014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9894804,"threshold_uncertainty_score":0.3109049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.182229344863412,"score_gpt":0.5195766904261504,"score_spread":0.3373473455627384,"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."}}