{"id":"W2611271516","doi":"10.4236/ojcm.2017.72005","title":"Measurement of the In-Plane Thermal Conductivity of Long Fiber Composites by Inverse Analysis","year":2017,"lang":"en","type":"article","venue":"Open Journal of Composite Materials","topic":"Thermal properties of materials","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Thermal conductivity; Materials science; Composite material; Thermal conduction; Composite number; Inverse; Transient (computer programming); Thermocouple; Thermal; Plane (geometry); Thermal conductivity measurement; Fiber; Heat transfer; Mechanics; Thermodynamics; Mathematics; Geometry; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004264863,0.0002225539,0.00124384,0.0001054166,0.000185154,0.0006586002,0.002914937,0.00007969948,0.003883408],"category_scores_gemma":[0.0001343299,0.0001409643,0.0001737594,0.00007844417,0.0004723693,0.0009159412,0.0009289319,0.00009219751,0.00004298256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007157081,"about_ca_system_score_gemma":0.0001119029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001329838,"about_ca_topic_score_gemma":0.00004863646,"domain_scores_codex":[0.9965627,0.0008980155,0.001346684,0.0001995345,0.0007401499,0.0002529224],"domain_scores_gemma":[0.9952596,0.00005135415,0.003307205,0.000859334,0.0004452715,0.00007724635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001076436,0.0001980554,0.009107993,0.0000902477,0.0002664486,0.00001111968,0.0002204302,0.0003540674,0.9884135,0.000007868374,0.0001934926,0.00006032431],"study_design_scores_gemma":[0.0009879697,0.0001138756,0.09518699,0.0002632169,0.0003041602,0.00001820803,0.00002557513,0.00000152733,0.9028817,0.00001675116,0.00006003464,0.0001399678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997579,0.00009216462,0.000008445343,0.000317673,0.0007673022,0.0004529525,0.0001580547,0.000002711576,0.0006216819],"genre_scores_gemma":[0.9993168,0.000005581465,0.0004677408,0.00003233775,0.00005611843,0.000003013275,0.000002263035,0.00001788294,0.00009832645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08607899,"threshold_uncertainty_score":0.9970272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04065039996944916,"score_gpt":0.2762392972382292,"score_spread":0.23558889726878,"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."}}