{"id":"W4394526637","doi":"10.6084/m9.figshare.17821328.v1","title":"EcoENERGY for Homes Data","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Energy Efficiency and Management","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Geography","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":[0.0007859516,0.001799753,0.001074197,0.004403573,0.001018509,0.002044787,0.00219286,0.001826585,0.09726698],"category_scores_gemma":[0.00445444,0.0006269419,0.001327692,0.01008048,0.0003684133,0.001136716,0.001460309,0.001779256,0.08089093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003254769,"about_ca_system_score_gemma":0.005432426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3008079,"about_ca_topic_score_gemma":0.4116325,"domain_scores_codex":[0.9988458,0.0001129166,0.00009951849,0.0002285214,0.0004480464,0.0002651719],"domain_scores_gemma":[0.9972346,0.0004213922,0.0002390223,0.0004197109,0.001415646,0.0002695835],"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":[0.00002320836,0.00001048265,0.0005573597,0.000163548,0.00001353076,0.000009740722,0.00001088391,0.0002628173,0.00004051816,0.0004799886,0.9972199,0.001207913],"study_design_scores_gemma":[0.00009456201,0.000009027142,0.006927007,0.0001709716,0.00002026811,0.00002314005,0.00009031056,0.0003819445,0.0001851178,0.0007435262,0.9913249,0.00002934472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005243418,0.00001360834,0.00001611335,0.00002785451,0.000007271574,0.000003297193,0.9993541,0.00005297703,0.0004724054],"genre_scores_gemma":[0.0002453212,0.00002897149,0.0001074692,0.00002755359,0.000003308038,0.00002903785,0.9984529,0.00003564203,0.001069843],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3008079,"threshold_uncertainty_score":0.598114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0876326942550401,"score_gpt":0.3030447299148953,"score_spread":0.2154120356598552,"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."}}