{"id":"W2891462653","doi":"10.31472/ihe.3.2018.12","title":"FEATURES OF THE APPLICATION OF THE IQLAB PROGRAM FOR SOLVING THE INVERSE HEAT CONDUCTION PROBLEM FOR CHROMIUM-NICKEL CYLINDRICAL THERMOSONDES","year":2018,"lang":"en","type":"article","venue":"Industrial Heat Engineering","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thermocouple; Thermal conduction; Materials science; Inverse; Constant (computer programming); Mechanics; Inverse problem; Thermodynamics; Mathematics; Mathematical analysis; Geometry; Computer science; Composite material; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005180293,0.0005725668,0.0003012016,0.0002634367,0.0002811902,0.0003623884,0.001058626,0.0003988991,0.006954137],"category_scores_gemma":[0.001548722,0.0002178516,0.0002927769,0.0003426064,0.0003088035,0.000363466,0.0003900175,0.0005404001,0.001059853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001927752,"about_ca_system_score_gemma":0.0006980434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002075079,"about_ca_topic_score_gemma":0.001148805,"domain_scores_codex":[0.9997521,0.00006416781,0.00001393779,0.00004296916,0.00009614025,0.00003061878],"domain_scores_gemma":[0.9992436,0.0004036197,0.00003486267,0.0001276404,0.0001651657,0.00002508433],"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":[0.001887514,0.0007465542,0.009350941,0.001041724,0.00007530811,0.0008006853,0.001348842,0.2905448,0.2456816,0.01835535,0.01199201,0.4181746],"study_design_scores_gemma":[0.0001862141,0.0002572246,0.002910431,0.00003863979,0.00002196767,0.0002270374,0.00009872014,0.8543119,0.1209713,0.002181461,0.01875106,0.00004403894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1513992,0.0001059481,0.8035411,0.0001763329,0.00004150717,0.0002264092,0.0008669863,0.02699478,0.01664764],"genre_scores_gemma":[0.480085,0.00009522906,0.5117645,0.00005571932,0.00001384282,0.0005243421,0.001061687,0.002950667,0.003449011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006954137,"threshold_uncertainty_score":0.02326387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09408170690910886,"score_gpt":0.3480760829699907,"score_spread":0.2539943760608818,"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."}}