{"id":"W2131108739","doi":"10.1109/icassp.2011.5947176","title":"Cooperative Maximum Likelihood estimation for fluid flow dynamics in biosensor arrays","year":2011,"lang":"en","type":"article","venue":"","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Partial differential equation; Ordinary differential equation; Flow (mathematics); Fluid dynamics; Biosensor; Estimation theory; Inverse problem; Advection; Applied mathematics; Inverse; Mathematics; Nonlinear system; Least-squares function approximation; Mathematical optimization; Differential equation; Computer science; Mathematical analysis; Algorithm; Mechanics; Physics; Statistics; Materials science; Geometry","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":[0.0006294516,0.0001985922,0.0003194246,0.0001164604,0.00005510782,0.0000221703,0.0001780753,0.0001360922,0.0003963819],"category_scores_gemma":[0.001558274,0.0001683608,0.00007808665,0.0002598746,0.00006833673,0.0001581269,0.00005130915,0.0001439576,0.00008547056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002165868,"about_ca_system_score_gemma":0.00003909359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005992724,"about_ca_topic_score_gemma":0.0001768862,"domain_scores_codex":[0.9986398,0.0001171278,0.0004524982,0.0003008512,0.0001412308,0.0003484986],"domain_scores_gemma":[0.9986845,0.000672562,0.0001021803,0.0003154485,0.0001416235,0.00008372797],"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":[0.0002804763,0.00115259,0.0003846261,0.0004904775,0.0001262561,0.00001224317,0.005216276,0.0002476157,0.00347486,0.8301908,0.003890557,0.1545332],"study_design_scores_gemma":[0.0004633827,0.000122929,0.000009328459,0.00003035344,0.00001751041,0.000002795421,0.0003960574,0.4293049,0.01077235,0.5586585,0.00005291044,0.0001689043],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002920469,0.000006363291,0.9663432,0.0001675971,0.0003156585,0.0009016005,0.00002677242,0.0001453462,0.02917302],"genre_scores_gemma":[0.026656,0.000004224707,0.9727005,0.0001502342,0.00002799598,0.0001315676,0.00001172787,0.00004398582,0.0002737259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4290573,"threshold_uncertainty_score":0.6865554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08915494161793787,"score_gpt":0.3347268544486492,"score_spread":0.2455719128307113,"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."}}