{"id":"W4310191174","doi":"10.20944/preprints202211.0517.v1","title":"Adoption of Artificial Intelligence for Optimum Productivity in the Construction Industry","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"BIM and Construction Integration","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Bridge (graph theory); Productivity; Process (computing); Engineering; Plan (archaeology); Emerging technologies; Computer science; Engineering management; Knowledge management; Data science; Artificial intelligence; Software engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007009031,0.0003030323,0.0002163546,0.001791277,0.001226036,0.007067542,0.000791613,0.001308738,0.001969317],"category_scores_gemma":[0.01340115,0.0002579171,0.0002977753,0.002618883,0.003377306,0.004523941,0.002540734,0.001106448,0.0004664908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004844945,"about_ca_system_score_gemma":0.006089975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002411073,"about_ca_topic_score_gemma":0.003740653,"domain_scores_codex":[0.9921401,0.003246787,0.0003902888,0.0006725115,0.002705844,0.0008444947],"domain_scores_gemma":[0.9881259,0.006635997,0.001877469,0.001153311,0.001649365,0.0005579529],"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":[0.0002142123,0.0005032719,0.07581262,0.001114858,0.00008250574,0.0009366889,0.03480347,0.008821144,0.00996619,0.2311636,0.003136717,0.6334447],"study_design_scores_gemma":[0.0001349203,0.001107777,0.3123467,0.00250113,0.0002109128,0.001629828,0.08446791,0.02795846,0.0170689,0.2642279,0.2881494,0.000196095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6776287,0.003905633,0.03800712,0.01189853,0.00005645973,0.0001012829,0.00003035816,0.000174587,0.2681974],"genre_scores_gemma":[0.9927951,0.000680024,0.00527143,0.0001264876,0.00001356554,0.00001314158,0.000009220306,0.00001144494,0.001079622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007067542,"threshold_uncertainty_score":0.03706771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1162292442617029,"score_gpt":0.3239801327102164,"score_spread":0.2077508884485135,"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."}}