{"id":"W6908372716","doi":"10.26050/wdcc/wdtf_annex80_build_v1.0","title":"IEA EBC Annex 80 \"Typical and extreme weather datasets for studying the resilience of buildings to climate change\" (Version 1.0)","year":2024,"lang":"en","type":"dataset","venue":"World Data Center for Climate","topic":"German History and Society","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; National Research Council Canada","funders":"","keywords":"Overheating (electricity); Climate change; Extreme weather; Climate model; Geospatial analysis; Weather station; Resilience (materials science); Downscaling","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":[0.003220186,0.00190253,0.001228952,0.001249354,0.0007176507,0.001754804,0.003298266,0.001949631,0.02077995],"category_scores_gemma":[0.007744148,0.0007189935,0.002148288,0.003584849,0.0005712828,0.002643401,0.002524894,0.002444068,0.02105938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001390139,"about_ca_system_score_gemma":0.001771233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03632056,"about_ca_topic_score_gemma":0.02762904,"domain_scores_codex":[0.9976981,0.000661061,0.0002480258,0.0004274416,0.000679698,0.0002856835],"domain_scores_gemma":[0.9960216,0.0006377723,0.0002436373,0.001508581,0.001207495,0.0003809798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003859903,0.0001817854,0.004250155,0.0007774664,0.0001169467,0.00004420921,0.00008040272,0.008367456,0.0007442305,0.002191292,0.9762656,0.006594507],"study_design_scores_gemma":[0.001428475,0.0001962905,0.05017476,0.0004858655,0.0001256321,0.0001260274,0.0004746573,0.0379492,0.004613746,0.005331224,0.8988737,0.0002202756],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003276336,0.00007648911,0.001388895,0.0002933915,0.0001332365,0.000133992,0.9879321,0.003974618,0.002790937],"genre_scores_gemma":[0.005460457,0.00006152377,0.00370279,0.00009242791,0.00002042574,0.0003820413,0.9890503,0.0005768503,0.0006531407],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03632056,"threshold_uncertainty_score":0.0722183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289868248645628,"score_gpt":0.313063945328546,"score_spread":0.1840771204639832,"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."}}