{"id":"W4411430568","doi":"10.25259/jksus_523_2025","title":"Analyzing the relationship between three-dimensional architectural landscapes and urban carbon emissions using machine learning approaches","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Science","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Carbon fibers; Correlation coefficient; Particle swarm optimization; Greenhouse gas; Support vector machine; Random forest; Environmental science; Computer science; Index (typography); Mean absolute percentage error; Mean squared error; Statistics; Mathematics; Algorithm; Artificial intelligence; Machine learning; Ecology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001675256,0.00007492923,0.0001259072,0.0001886728,0.001509525,0.00004640252,0.0003139243,0.00003103057,0.000009582347],"category_scores_gemma":[0.0005313432,0.00005371388,0.00003852796,0.0007953224,0.0004840957,0.0003227189,0.0002544832,0.000476585,3.971681e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231011,"about_ca_system_score_gemma":0.0001531015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006706758,"about_ca_topic_score_gemma":0.0002571621,"domain_scores_codex":[0.9990129,0.0001342085,0.0001689507,0.0001443609,0.000325133,0.0002144335],"domain_scores_gemma":[0.9989552,0.0005562986,0.0002319614,0.00008735551,0.00001775048,0.0001513724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000143786,0.000006346702,0.9847017,0.000006013713,0.000005003097,0.000003245511,0.0006700804,0.01311699,0.0002856835,0.0002799122,0.0000079687,0.0009027117],"study_design_scores_gemma":[0.0001613011,0.00002933376,0.9312373,0.00009328282,0.00003842742,0.00002231135,0.0003035258,0.06721194,0.00003747696,0.0005633063,0.0002408459,0.0000609465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934554,0.0001094046,0.002204316,0.003610045,0.00004427703,0.00004672159,9.642795e-7,0.000007306865,0.000521628],"genre_scores_gemma":[0.9969385,0.000002449284,0.002872066,0.00006080748,0.00002802237,1.468544e-8,2.633063e-7,0.000001935435,0.00009597377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05409496,"threshold_uncertainty_score":0.9997904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0794801239377602,"score_gpt":0.286170200317627,"score_spread":0.2066900763798667,"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."}}