{"id":"W2296546292","doi":"10.5555/2872550.2872562","title":"Comparison of data analysis tools for trending thermal comfort parameters","year":2015,"lang":"en","type":"article","venue":"","topic":"Multidisciplinary Science and Engineering Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Thermal comfort; Artificial neural network; Air temperature; Regression analysis; Computer science; Airflow; Thermal; Humidity; Environmental science; Wireless sensor network; Engineering; Meteorology; Artificial intelligence; Machine learning; Geography; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005655612,0.0000772225,0.0003675516,0.0004744336,0.00006974138,0.0002461328,0.002051365,0.00003433855,0.0001285624],"category_scores_gemma":[0.002338917,0.00004845304,0.0001129167,0.001849398,0.00009864894,0.001212224,0.0004410289,0.00005868696,0.00002522051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001646239,"about_ca_system_score_gemma":0.00006218945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000626556,"about_ca_topic_score_gemma":0.00007018338,"domain_scores_codex":[0.9974896,0.00004608696,0.0005051852,0.000410923,0.001291335,0.0002569201],"domain_scores_gemma":[0.9962169,0.002144462,0.0001047786,0.001195033,0.000161687,0.0001771593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009246069,0.0001423567,0.4905505,0.000005962997,0.0001910226,0.00000121704,0.001729663,0.2935504,0.001101314,0.0007571584,0.01608951,0.1957885],"study_design_scores_gemma":[0.0001940682,0.0001053536,0.02647951,0.000001931943,0.00003975696,2.25526e-7,0.003690426,0.9666494,0.0007694129,0.0003961849,0.001591603,0.00008214202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7894191,0.00002942048,0.2073172,0.0002289433,0.0001053797,0.0001269574,0.00006105888,0.00001847904,0.002693537],"genre_scores_gemma":[0.9615851,5.79678e-7,0.03748119,0.000007486271,0.00001255038,0.000004922725,0.00004988724,0.000003179073,0.0008551022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.673099,"threshold_uncertainty_score":0.381198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8353881424271626,"score_gpt":0.614234235562003,"score_spread":0.2211539068651597,"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."}}