{"id":"W4388665935","doi":"10.12783/asc38/36637","title":"USING MACHINE LEARNING TO PREDICT MECHANICAL PROPERTIES OF LONG FIBER THERMOPLASTICS BASED ON MANUFACTURING PROCESS PARAMETERS","year":2023,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science; Fiber; Composite material; Torque; Ultimate tensile strength; Random forest; Plastics extrusion; Extrusion; Linear regression; Mechanical engineering; Machine learning; Computer science; Engineering","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":[0.001513833,0.001263577,0.0006889727,0.001270248,0.0002626855,0.0008351178,0.000592626,0.001020082,0.0006770049],"category_scores_gemma":[0.003908284,0.0004681899,0.001099364,0.0009171763,0.0003128746,0.0008255092,0.0003132128,0.001005816,0.0003724908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006889067,"about_ca_system_score_gemma":0.0005644032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009566439,"about_ca_topic_score_gemma":0.00954536,"domain_scores_codex":[0.9995779,0.0001072286,0.00003951663,0.0001307017,0.0001014107,0.00004322085],"domain_scores_gemma":[0.9976414,0.0015904,0.0003060028,0.0001512888,0.0002763048,0.00003464044],"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":[0.0001637581,0.0003824111,0.02444168,0.00009328654,0.000114123,0.00007815984,0.00004658608,0.9119444,0.006209191,0.0001341063,0.0003186628,0.05607362],"study_design_scores_gemma":[0.000003749244,0.00007049588,0.005752812,0.00000749521,0.00001177553,0.00001275249,0.00001117667,0.9916167,0.002250622,0.0001263422,0.000125432,0.00001072217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8950675,0.0006260414,0.1008001,0.0001677656,0.00003634526,0.000102946,0.0009393962,0.000925171,0.001334716],"genre_scores_gemma":[0.9759541,0.0001478865,0.02200817,0.00002236361,0.000009413951,0.00008521835,0.001179966,0.00002301847,0.000569884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009566439,"threshold_uncertainty_score":0.01902151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820811880692956,"score_gpt":0.2282568076546395,"score_spread":0.2000486888477099,"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."}}