{"id":"W1574085630","doi":"10.1007/978-3-642-15461-4_43","title":"Inverse Modeling in Geoenvironmental Engineering Using a Novel Particle Swarm Optimization Algorithm","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Particle swarm optimization; Computer science; Algorithm; Mathematical optimization; Inverse; Convergence (economics); Multi-swarm optimization; Inverse problem; Premature convergence; Population; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004172903,0.000661369,0.000951155,0.0003808242,0.0004398547,0.0008423698,0.0009846705,0.001681432,0.001606989],"category_scores_gemma":[0.0008986134,0.0005111702,0.0008665852,0.0007532409,0.0004626798,0.001086752,0.0007461755,0.0009476416,0.0004970969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002964298,"about_ca_system_score_gemma":0.0005390571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003536803,"about_ca_topic_score_gemma":0.002942432,"domain_scores_codex":[0.9998318,0.00004361114,0.000009725283,0.00003072155,0.00007511151,0.000008986341],"domain_scores_gemma":[0.9997641,0.0001256311,0.00002259582,0.00002367141,0.000055056,0.000008852591],"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":[0.00001981343,0.00004432509,0.0002150679,0.00005718625,0.00002921192,0.00005265271,0.0000432671,0.9371282,0.003045796,0.01157511,0.0008747223,0.04691468],"study_design_scores_gemma":[0.000001933361,0.000003539086,0.00001900028,0.000001327555,0.000002274136,0.000006577418,0.000001741427,0.9986519,0.0001609166,0.0007673504,0.0003814792,0.000001838687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001504067,0.0000706366,0.9972292,0.00003585477,0.00003650358,0.00001069658,0.000007286701,0.00006812805,0.001037658],"genre_scores_gemma":[0.1569511,0.0004412857,0.8363163,0.00009537376,0.0001183603,0.0002113545,0.00009208686,0.00011043,0.005663555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003536803,"threshold_uncertainty_score":0.007032394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145264987398007,"score_gpt":0.2341611800900542,"score_spread":0.2127085302160741,"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."}}