{"id":"W2108352392","doi":"10.5897/jmer.9000011","title":"Calibrating the multiple orifice mathematical model using physical scale model foam at low Reynolds number","year":2010,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Heat and Mass Transfer in Porous Media","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reynolds number; Body orifice; Materials science; Mechanics; Range (aeronautics); Calibration; Flow (mathematics); Ceramic; Orifice plate; Scale model; Mechanical engineering; Mathematics; Composite material; Engineering; Turbulence; Physics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001277732,0.000326227,0.0003759114,0.00009384249,0.0002804942,0.0001167534,0.0006277767,0.0003263497,0.00008111534],"category_scores_gemma":[0.0004658747,0.0002584264,0.0001613,0.0004354611,0.0001056171,0.0001985526,0.0002362572,0.002241048,0.0001545774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001790958,"about_ca_system_score_gemma":0.00007491666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001319396,"about_ca_topic_score_gemma":0.00002308449,"domain_scores_codex":[0.9969697,0.00004836687,0.0003957228,0.0004151015,0.001008402,0.001162688],"domain_scores_gemma":[0.9978926,0.0008210136,0.00001111792,0.0007396639,0.0001156884,0.0004198785],"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":[0.000008046432,0.00005332619,0.000006330743,0.0001523708,0.00001698357,0.000006412824,0.0004719378,0.5314572,0.4625165,0.004810442,0.0002134364,0.0002869706],"study_design_scores_gemma":[0.0002634064,0.00001185953,0.000003234905,0.00005793576,0.00001416192,0.00002356109,0.00003000798,0.905044,0.09144841,0.002727472,0.0001033328,0.0002726465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6840172,0.00000993974,0.3140958,0.00008097035,0.0002405969,0.0003166688,0.000016379,0.0004373193,0.0007851629],"genre_scores_gemma":[0.97209,0.00000789921,0.02687122,0.0000152032,0.0004450002,0.0001208056,0.000006494231,0.000168987,0.0002743935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3735868,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04267130957382975,"score_gpt":0.3073702562703463,"score_spread":0.2646989466965166,"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."}}