{"id":"W2807447765","doi":"10.3970/cmes.2007.020.099","title":"An accurate refinement scheme for inverse heat source location identifications","year":2007,"lang":"en","type":"article","venue":"HKBU Institutional Repository (Hong Kong Baptist University)","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scheme (mathematics); Inverse; Mathematics; Geometry; Mathematical analysis","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":[0.0001874263,0.0001595439,0.0001120718,0.0003358123,0.0006522097,0.00005621897,0.0002468181,0.0001231632,0.00001278175],"category_scores_gemma":[0.00001567734,0.0002006887,0.00008423406,0.0004657584,0.0001823208,0.0004705577,0.00002054863,0.0001369086,0.000004723413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003368935,"about_ca_system_score_gemma":0.00008749526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009819429,"about_ca_topic_score_gemma":0.0001028325,"domain_scores_codex":[0.9990992,0.00001954912,0.0002166712,0.0002571048,0.0001782664,0.0002292147],"domain_scores_gemma":[0.9992649,0.00005333705,0.00004400845,0.0003033425,0.0001932666,0.0001411583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003316991,0.0003969778,0.004035391,0.0003586187,0.000313023,0.0002052156,0.0006258691,0.1754435,0.6884254,0.1244856,0.002244288,0.003134346],"study_design_scores_gemma":[0.002868023,0.0003744155,0.09515128,0.0005679937,0.0004827593,0.000281431,0.002582662,0.176428,0.2542585,0.00086357,0.463771,0.002370386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3226374,0.0000468358,0.6698094,0.00001247296,0.0004698524,0.000282146,0.00006656334,0.0006135107,0.006061857],"genre_scores_gemma":[0.9916483,0.00001260602,0.006958614,0.00001935341,0.000149015,0.000006447534,0.000126343,0.00001832263,0.001060992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6690109,"threshold_uncertainty_score":0.8183847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130139359515593,"score_gpt":0.2136895202417172,"score_spread":0.2023881266465613,"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."}}