{"id":"W4392269209","doi":"10.1016/j.jobe.2024.108784","title":"An application of the Random Forest algorithm for the prediction of Solar Envelope ‘Floor Space Index’ based on spatiotemporal parameters","year":2024,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Envelope (radar); Index (typography); Random forest; Algorithm; Space (punctuation); Computer science; Environmental science; Artificial intelligence; Telecommunications","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":[0.001348335,0.0007772642,0.001290301,0.001158863,0.0005364059,0.000552643,0.0009899244,0.0009490435,0.0009329249],"category_scores_gemma":[0.00211948,0.0003571662,0.001076205,0.001183291,0.0001696301,0.0007254836,0.0003697782,0.0007006033,0.0004411276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002579036,"about_ca_system_score_gemma":0.0009246586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02143652,"about_ca_topic_score_gemma":0.01431598,"domain_scores_codex":[0.9996341,0.00008340871,0.0000289237,0.0001114836,0.00008184796,0.00006006828],"domain_scores_gemma":[0.9990544,0.0005185074,0.0000486791,0.00005216547,0.0002919426,0.00003428607],"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":[0.0003652864,0.0002651703,0.007508459,0.0001079593,0.0002306104,0.0001864448,0.00005268436,0.5254759,0.006698088,0.001067224,0.003297812,0.4547444],"study_design_scores_gemma":[0.0000058476,0.00001772279,0.0006466448,0.000003216271,0.00001156727,0.00001932619,0.000004148982,0.9985471,0.0003828263,0.000206872,0.0001499534,0.000004765505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06939615,0.0007649534,0.9265031,0.0001337067,0.0001243125,0.0000681106,0.0003632678,0.001905264,0.0007410613],"genre_scores_gemma":[0.5861037,0.0004804092,0.4102533,0.00007036028,0.0001142166,0.0001156977,0.00138143,0.0001233716,0.00135736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02143652,"threshold_uncertainty_score":0.04262346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009000076383261274,"score_gpt":0.2262119782436993,"score_spread":0.2172119018604381,"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."}}