{"id":"W4310790240","doi":"10.1061/jcemd4.coeng-12811","title":"A Deep-Learning Classification Framework for Reducing Communication Errors in Dynamic Hand Signaling for Crane Operation","year":2022,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Deep learning; Set (abstract data type); Machine learning; Field (mathematics); SIGNAL (programming language); Object (grammar); Recurrent neural network; Task (project management); Operator (biology); Artificial neural network; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0008567295,0.00006367345,0.0001199354,0.0003117525,0.0002207715,0.0001087434,0.0001334044,0.00002519721,0.000001584553],"category_scores_gemma":[0.00004910561,0.00006922393,0.0000394177,0.0001727995,0.000009684361,0.0002317729,0.00003856711,0.000164109,9.179998e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001066566,"about_ca_system_score_gemma":0.00001218225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000103263,"about_ca_topic_score_gemma":7.223949e-7,"domain_scores_codex":[0.9992764,0.00005535434,0.0003384967,0.0001090631,0.000130267,0.00009040257],"domain_scores_gemma":[0.9994379,0.0001280426,0.0002350308,0.00009244515,0.00007933199,0.00002726888],"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":[0.00002907985,0.00002452098,0.000054328,0.0001883206,0.0000533565,0.000001557305,0.001409818,0.8199118,0.002718715,0.04099156,0.000009584985,0.1346073],"study_design_scores_gemma":[0.0007141627,0.0001106603,0.0005016061,0.0001787755,0.00002080361,0.0001145647,0.001636952,0.9911887,0.000143908,0.00164327,0.00365147,0.00009512602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02949138,0.0003288835,0.968892,0.0005053302,0.0004468515,0.0003039196,5.364427e-7,0.00001888931,0.00001225893],"genre_scores_gemma":[0.6883155,0.00008977061,0.3114623,0.00001036494,0.00002414016,0.00007899237,0.000003139662,0.000005140532,0.00001067109],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.658824,"threshold_uncertainty_score":0.282287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240595347423445,"score_gpt":0.2461388632781331,"score_spread":0.2337329098038987,"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."}}