{"id":"W1969884402","doi":"10.1016/j.autcon.2014.07.006","title":"Image dataset development for measuring construction equipment recognition performance","year":2014,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Science for Equity, Empowerment and Development Division","keywords":"Excavator; Computer science; Loader; Cognitive neuroscience of visual object recognition; Artificial intelligence; Robustness (evolution); Correctness; Boom; Computer vision; Engineering; Object (grammar)","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":[0.0003288567,0.000149689,0.0001394068,0.0002144645,0.0001292934,0.0000516125,0.00006491219,0.00008529916,0.00002433736],"category_scores_gemma":[0.0000461016,0.0001677488,0.00002258969,0.000143297,0.0000446969,0.0006139183,0.00001368646,0.0001080161,0.00002891871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002946488,"about_ca_system_score_gemma":0.00002733962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003670383,"about_ca_topic_score_gemma":0.000007858769,"domain_scores_codex":[0.9990115,0.00002102793,0.0003950306,0.0001846927,0.0001460308,0.0002416808],"domain_scores_gemma":[0.9996334,0.00003605554,0.00008161728,0.0001320494,0.00008117987,0.00003571704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001996529,0.00000694237,0.002266883,0.0003353125,0.00002141645,2.65939e-7,0.0002765785,0.004769531,0.01196605,0.0002834681,0.000415314,0.9796383],"study_design_scores_gemma":[0.003063962,0.00009532005,0.04352825,0.0007372639,0.0000460844,0.0001864994,0.0007456358,0.5838383,0.3317359,0.002961565,0.03197222,0.001089],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7662995,0.000009075609,0.2299346,0.00001861038,0.002191668,0.0003491387,0.00003538356,0.0003314796,0.0008304372],"genre_scores_gemma":[0.7723758,0.00001654965,0.2265901,0.00001307904,0.0002180789,0.000154829,0.0006082213,0.00002090407,0.000002402393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9785493,"threshold_uncertainty_score":0.6840596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584492363970968,"score_gpt":0.2195015391639147,"score_spread":0.203656615524205,"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."}}