{"id":"W2031592926","doi":"10.1117/12.644852","title":"Optimization procedures for the estimation of phase portrait parameters of orientation fields","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Optical Polarization and Ellipsometry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Initialization; Simulated annealing; Maxima and minima; Orientation (vector space); Non-linear least squares; Computer science; Pixel; Phase portrait; Algorithm; Linear least squares; Least-squares function approximation; Nonlinear system; Estimation theory; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","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":[0.0003711524,0.0002125824,0.0003113312,0.0001174323,0.00005554897,0.00004610149,0.0004099725,0.0001696092,0.000007694549],"category_scores_gemma":[0.0005795231,0.0001678891,0.000386316,0.0004121219,0.0001692961,0.0003990767,0.00003282208,0.0001485352,2.363128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006193866,"about_ca_system_score_gemma":0.00002568269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001007066,"about_ca_topic_score_gemma":2.433355e-7,"domain_scores_codex":[0.998319,1.753164e-8,0.0008113215,0.0001918537,0.0004500725,0.0002277548],"domain_scores_gemma":[0.9982229,0.0002455333,0.000347356,0.00005241936,0.001082795,0.00004901739],"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":[0.0001267341,0.0002250587,0.0001582959,0.001758683,0.0003672601,1.498169e-8,0.0002018251,0.4953853,0.1496049,0.3489177,0.002254038,0.001000184],"study_design_scores_gemma":[0.00110501,0.000230817,0.0002628408,0.0001510248,0.0001818785,0.000002387899,0.0004200898,0.8411046,0.154848,0.001390511,0.0001321918,0.0001706935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966718,0.0001095396,0.03096042,0.0005172737,0.0002174096,0.0007337565,0.00007704578,0.00007145206,0.0005951106],"genre_scores_gemma":[0.7945745,0.00005350128,0.2050042,0.00002366308,0.0001162864,0.0001089997,0.00003522593,0.00004036156,0.00004324808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3475272,"threshold_uncertainty_score":0.6846317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009496210306644479,"score_gpt":0.2374776440824233,"score_spread":0.2279814337757788,"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."}}