{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001714274,0.0000879956,0.0001118318,0.0003562055,0.00009894268,0.00005063384,0.00002161669,0.00008823952,0.000009768585],"category_scores_gemma":[0.00003952222,0.00010341,0.00001914725,0.0003309596,0.00002658139,0.0002517416,0.000007195428,0.00009005556,0.000002182397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276701,"about_ca_system_score_gemma":0.00001719991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001115427,"about_ca_topic_score_gemma":0.00001999444,"domain_scores_codex":[0.9993952,0.00005965763,0.0002569678,0.000127659,0.00006999877,0.00009051089],"domain_scores_gemma":[0.9998012,0.00002816189,0.00004335292,0.00007881189,0.0000303247,0.00001816762],"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":[0.00001126887,0.000002265951,0.0008889703,0.00003502325,0.000007301174,1.655951e-7,0.0001474382,0.9603816,0.02173824,0.002576014,0.000003480325,0.01420823],"study_design_scores_gemma":[0.000967171,0.000002413675,0.001833165,0.00005248329,0.00001178449,0.0000124412,0.0001143926,0.9917182,0.002668269,0.002508343,0.00002290601,0.00008847484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4848461,0.00003897766,0.5143156,0.00001685336,0.0002136452,0.0001077796,1.350113e-7,0.0001617244,0.0002992163],"genre_scores_gemma":[0.9834051,0.00002061443,0.01649123,0.00001491525,0.00002204255,0.00001758458,0.000004741501,0.00001093195,0.00001287201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.498559,"threshold_uncertainty_score":0.4216936,"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."}}