{"id":"W2029178229","doi":"10.1108/09699980410558575","title":"Innovative 3D‐modelling for selecting and locating mobile cranes","year":2004,"lang":"en","type":"article","venue":"Engineering Construction & Architectural Management","topic":"BIM and Construction Integration","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Concordia University","funders":"","keywords":"Lift (data mining); Animation; Computer science; Selection (genetic algorithm); Graphics; Industrial engineering; Engineering; Systems engineering; Data mining; Artificial intelligence; Computer graphics (images)","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":[0.00008827689,0.0002120334,0.0001530254,0.0002668818,0.0001458465,0.00007222716,0.00006277322,0.00005138664,0.000006174157],"category_scores_gemma":[0.000009176394,0.0002231182,0.00003694464,0.0003970111,0.00004495034,0.0001448882,0.00002255645,0.0001830003,0.000002359267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058643,"about_ca_system_score_gemma":0.000007363343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000933753,"about_ca_topic_score_gemma":0.000002836678,"domain_scores_codex":[0.999121,0.00000520987,0.0002781662,0.000228688,0.0001068684,0.0002601181],"domain_scores_gemma":[0.9997015,0.00003640661,0.00003949215,0.0001045227,0.00006996439,0.00004809392],"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":[0.000005208015,0.000002206927,0.00003213247,0.0001885446,0.00006408103,5.84822e-7,0.0002852354,0.8181872,0.001355866,0.01357414,0.000002052405,0.1663028],"study_design_scores_gemma":[0.001513263,0.0001091351,0.000373562,0.0003323706,0.00006626293,0.0003638073,0.001236071,0.9765406,0.009836389,0.00381452,0.005032949,0.0007810697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3124138,0.0001505348,0.6856334,0.00002469034,0.0005505359,0.000373451,0.000003035489,0.0004845486,0.0003660398],"genre_scores_gemma":[0.7587309,0.00002274962,0.2408704,0.00001004249,0.0001234396,0.0001889047,0.000008739522,0.00002955227,0.00001519645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4463172,"threshold_uncertainty_score":0.9098498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005183567148387535,"score_gpt":0.1866415205978619,"score_spread":0.1814579534494744,"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."}}