{"id":"W4388971245","doi":"10.3390/buildings13122933","title":"Identification of Occupant Dissatisfaction Factors in Newly Constructed Apartments: Text Mining and Semantic Network Analysis","year":2023,"lang":"en","type":"article","venue":"Buildings","topic":"BIM and Construction Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Apartment; Post-occupancy evaluation; Identification (biology); Computer science; Receipt; Lexical analysis; Engineering; World Wide Web; Architectural engineering; Artificial intelligence; Civil 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000749047,0.0006094214,0.0004289968,0.004578339,0.0005255062,0.0007557372,0.0004810546,0.0006453715,0.0005283162],"category_scores_gemma":[0.002462934,0.0001344891,0.0006485549,0.003306309,0.0002698095,0.0009927168,0.0005593347,0.0003868094,0.0002433783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007006166,"about_ca_system_score_gemma":0.0006194749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008991383,"about_ca_topic_score_gemma":0.0135698,"domain_scores_codex":[0.9991568,0.0001391913,0.0001422792,0.0002279597,0.0002470151,0.00008660622],"domain_scores_gemma":[0.9984029,0.0008007555,0.0003944231,0.00005932888,0.0002691551,0.00007353003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001188749,0.001367656,0.5286286,0.002569039,0.0005058287,0.004978256,0.007519502,0.01857678,0.04544847,0.002859324,0.0133278,0.3730299],"study_design_scores_gemma":[0.00003501353,0.0002836425,0.7007608,0.0002603845,0.0003543087,0.001480022,0.01749926,0.2441847,0.01177708,0.00260159,0.02065862,0.0001045826],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598562,0.0008124767,0.02667849,0.0005825267,0.00006532067,0.0002923859,0.008921154,0.0002497889,0.002541703],"genre_scores_gemma":[0.9531953,0.0006191835,0.03046646,0.00008757176,0.00006139641,0.0003318705,0.01369561,0.00001830656,0.00152425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008991383,"threshold_uncertainty_score":0.01787812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008826616983771554,"score_gpt":0.2292085714907362,"score_spread":0.2203819545069647,"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."}}