{"id":"W4401847930","doi":"10.1108/ci-08-2023-0210","title":"Where lean construction and offsite construction meet: a bibliographic scientometric analysis","year":2024,"lang":"en","type":"article","venue":"Construction Innovation","topic":"BIM and Construction Integration","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lean construction; Computer science; Engineering; Construction engineering; Construction industry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01943535,0.0008531282,0.002040099,0.2164421,0.002789088,0.01607053,0.001101064,0.001144865,0.006109436],"category_scores_gemma":[0.09382231,0.0005135146,0.002156025,0.2883571,0.002415622,0.009366786,0.005499767,0.0007880635,0.001009269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006092086,"about_ca_system_score_gemma":0.01186431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009191092,"about_ca_topic_score_gemma":0.009146483,"domain_scores_codex":[0.9699332,0.007761307,0.006613785,0.001741293,0.01284136,0.001109128],"domain_scores_gemma":[0.9180641,0.04667266,0.01275886,0.00325196,0.01812965,0.001122699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004183211,0.0002563523,0.2655745,0.04261756,0.001839968,0.002639743,0.05341199,0.003046521,0.003662994,0.04563202,0.02225064,0.5586494],"study_design_scores_gemma":[0.00009558588,0.0004282878,0.5365254,0.02934831,0.00308227,0.00301636,0.166207,0.007064548,0.005279686,0.02928061,0.2193573,0.0003146578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7010261,0.09808735,0.02483525,0.008296634,0.0006513839,0.004889798,0.03168042,0.0004881281,0.1300449],"genre_scores_gemma":[0.9206088,0.04178317,0.02223296,0.0003068701,0.0003735366,0.002225966,0.00935478,0.0001279376,0.002986051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.783558,"threshold_uncertainty_score":0.1027852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010837376935702,"score_gpt":0.2318131495620153,"score_spread":0.2217047757926583,"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."}}