{"id":"W3138754412","doi":"10.1002/asjc.2494","title":"Tensor product‐based model transformation approach to tower crane systems modeling","year":2021,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii","keywords":"Nonlinear system; Control theory (sociology); Model transformation; Position (finance); Transformation (genetics); Payload (computing); Tower; Nonlinear model; Computer science; Applied mathematics; Control engineering; Mathematics; Engineering; Physics; Artificial intelligence; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.0003234816,0.00009340153,0.0002081343,0.0001032303,0.00008074482,0.0001884263,0.0003480949,0.00002148536,0.00000128546],"category_scores_gemma":[0.00002074458,0.00008322981,0.0001134162,0.00030591,0.000006240974,0.0003948269,0.00001122428,0.0001295215,0.000004853665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003153826,"about_ca_system_score_gemma":0.0003261216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001232647,"about_ca_topic_score_gemma":3.139428e-7,"domain_scores_codex":[0.9988484,0.00005225849,0.000431356,0.000160358,0.0003669974,0.0001406164],"domain_scores_gemma":[0.9987058,0.00002791783,0.0001444953,0.0002520687,0.0007347155,0.0001349943],"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":[0.00001183978,0.00009119044,0.000002277585,0.00001067734,0.00002150108,0.000002196923,0.0004756276,0.939075,0.0009070823,0.05418743,0.000152558,0.005062646],"study_design_scores_gemma":[0.000692887,0.00002759334,0.00001908023,0.00002382204,0.00001677977,0.00005966202,0.00006175959,0.9936422,0.00008437963,0.004872622,0.0004064533,0.00009276629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001019724,0.000127603,0.9848443,0.01002973,0.0001301341,0.0002148882,0.000005382231,0.0000180768,0.003610142],"genre_scores_gemma":[0.9357478,0.000001095092,0.06356972,0.0004681917,0.0001466177,0.0000154164,0.000002764407,0.000006705421,0.00004171141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9347281,"threshold_uncertainty_score":0.3394013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925169658569143,"score_gpt":0.2396465095091535,"score_spread":0.2203948129234621,"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."}}