{"id":"W2176356482","doi":"10.1109/tia.2013.2244544","title":"Model-Based Virtual Sensors and Core-Temperature Observers in Thermoforming Applications","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Advancements in Photolithography Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Thermoforming; Temperature measurement; Temperature control; Controllability; Core (optical fiber); Observer (physics); Mechanical engineering; Process (computing); Robustness (evolution); Computer science; Infrared heater; Engineering; Control theory (sociology); Infrared; Artificial intelligence; Control (management); Chemistry; Optics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005733886,0.0002373013,0.0001627611,0.0002675303,0.0002009287,0.0000417173,0.0001785656,0.0003999512,0.0000729873],"category_scores_gemma":[8.338295e-7,0.000262031,0.00005603911,0.0006717546,0.0001193751,0.0002479426,0.000001457046,0.0009412013,0.0000193318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123365,"about_ca_system_score_gemma":0.00002783517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002040917,"about_ca_topic_score_gemma":0.00001482209,"domain_scores_codex":[0.9989712,0.00001266597,0.0002964479,0.0003044167,0.0001390047,0.0002763224],"domain_scores_gemma":[0.9993038,0.00006950417,0.00003768836,0.0004148542,0.00005286466,0.0001213579],"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":[0.000005993535,0.0002175897,0.0002365831,0.00004873745,0.00003943023,5.492453e-7,0.0001014094,0.921707,0.0386555,0.001284461,0.0002108675,0.03749192],"study_design_scores_gemma":[0.001115182,0.00009270575,0.001308787,0.0001347866,0.00008599718,0.00001476084,0.0008045241,0.8310806,0.1545279,0.00448518,0.005098717,0.00125086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1856951,0.00002728494,0.8108313,0.00008570102,0.00003513777,0.001713893,0.0001121896,0.0006268764,0.0008724632],"genre_scores_gemma":[0.9797524,0.00005196278,0.009424469,0.0001081843,0.00002424053,0.01044192,0.00001369637,0.00005089155,0.0001322106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8014069,"threshold_uncertainty_score":0.9999832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719172690198114,"score_gpt":0.2489703172380287,"score_spread":0.2317785903360475,"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."}}