{"id":"W4410219594","doi":"10.1016/j.autcon.2025.106252","title":"Rebar grasp detection using a synthetic model generator and domain randomization","year":2025,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Precast/Prestressed Concrete Institute; McGill University; Princeton University","keywords":"GRASP; Rebar; Generator (circuit theory); Randomization; Domain (mathematical analysis); Computer science; Engineering; Artificial intelligence; Engineering drawing; Structural engineering; Mathematics; Power (physics); Programming language; Medicine; Physics; Clinical trial","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007981502,0.001257959,0.000550203,0.0008779835,0.0002657167,0.0008875626,0.001402315,0.001145318,0.001809671],"category_scores_gemma":[0.002502954,0.0004511796,0.001002467,0.0005443227,0.0005126909,0.0008111174,0.00105194,0.0009116929,0.0008477451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006006889,"about_ca_system_score_gemma":0.0007213579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002693034,"about_ca_topic_score_gemma":0.00431306,"domain_scores_codex":[0.9995783,0.00008545793,0.00002178131,0.0001673994,0.00009891507,0.00004817036],"domain_scores_gemma":[0.9990928,0.0004300321,0.00009230567,0.000220231,0.0001223345,0.00004235599],"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":[0.0003421738,0.0001863005,0.003671352,0.000256516,0.00007258283,0.0003703239,0.0001230258,0.8474343,0.03141523,0.003254302,0.005355218,0.1075187],"study_design_scores_gemma":[0.00001177957,0.00005236811,0.0003867205,0.000009498338,0.000005148269,0.0000705585,0.00002316121,0.9879448,0.009161731,0.001088083,0.001235917,0.00001022069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2181141,0.0004261966,0.7638171,0.0003139554,0.0001333163,0.000352589,0.002170417,0.01169397,0.002978335],"genre_scores_gemma":[0.653253,0.0002331915,0.3355752,0.0001933482,0.00002335428,0.0004651336,0.006721682,0.0008015956,0.002733499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002693034,"threshold_uncertainty_score":0.006053925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008215319076667293,"score_gpt":0.2203733793679224,"score_spread":0.2121580602912551,"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."}}