{"id":"W4322630678","doi":"10.1108/ci-10-2022-0279","title":"Forecasting demand in the residential construction industry using machine learning algorithms in Jordan","year":2023,"lang":"en","type":"article","venue":"Construction Innovation","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demand forecasting; Identification (biology); Quarter (Canadian coin); Computer science; Feature selection; Supply and demand; Machine learning; Artificial neural network; Algorithm; Artificial intelligence; Operations research; Engineering; Economics","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":[0.000918184,0.0003787345,0.0003140626,0.001532942,0.0002070513,0.0007173943,0.0003859561,0.0003597694,0.0005608682],"category_scores_gemma":[0.002126958,0.000158805,0.0004252475,0.001560747,0.0001324406,0.0007284915,0.0003003227,0.0002916513,0.0001935484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008107898,"about_ca_system_score_gemma":0.0008713349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364078,"about_ca_topic_score_gemma":0.01116625,"domain_scores_codex":[0.9997113,0.0001104439,0.00003149909,0.00004070342,0.00008050866,0.00002550539],"domain_scores_gemma":[0.9990698,0.0005065962,0.0001148011,0.00002611269,0.0002607324,0.00002199416],"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":[0.0002119216,0.0002728052,0.1131661,0.0003942021,0.0001278633,0.0003844191,0.0004123613,0.7226875,0.002126573,0.002425002,0.002836327,0.1549549],"study_design_scores_gemma":[0.00001098121,0.00006770307,0.01693289,0.0000504213,0.00002173471,0.00003604596,0.0005847258,0.9783669,0.001323333,0.001088786,0.00150026,0.00001622096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695382,0.00156232,0.02353872,0.0006157272,0.00001942348,0.00004343616,0.0005534787,0.00009911461,0.004029655],"genre_scores_gemma":[0.9847032,0.001095291,0.01246832,0.00003534382,0.00001569609,0.00003361398,0.0008642992,0.000009558019,0.0007745603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01364078,"threshold_uncertainty_score":0.0271228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1975978200127455,"score_gpt":0.3955627872916873,"score_spread":0.1979649672789418,"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."}}