{"id":"W7062193044","doi":"","title":"5s3: Worldwide wardrobes, inside consumers’ clothing systems","year":2022,"lang":"en","type":"other","venue":"Socio-Environmental Systems Modeling","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"Clothing; Work (physics); Clothing industry; Government (linguistics)","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":[0.0005261161,0.0007206962,0.0002544083,0.0005427555,0.0008934193,0.002499669,0.0008870076,0.0009342146,0.1328099],"category_scores_gemma":[0.001203932,0.0002682065,0.0006087265,0.001298651,0.0003888056,0.002275863,0.001962703,0.0008534909,0.0144011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092239,"about_ca_system_score_gemma":0.00095299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0338817,"about_ca_topic_score_gemma":0.05853621,"domain_scores_codex":[0.9997548,0.0001009102,0.000005884582,0.00003000846,0.00006416853,0.00004426959],"domain_scores_gemma":[0.9996094,0.00009657797,0.00001771081,0.00009208111,0.00009948057,0.0000846421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003515445,0.0001556167,0.006311646,0.0003577179,0.00004284216,0.000504669,0.00155867,0.1003953,0.001529707,0.2178315,0.5162029,0.154758],"study_design_scores_gemma":[0.00004832344,0.00006751355,0.004046121,0.0001602498,0.00002241587,0.0001286451,0.001663484,0.1201093,0.001617977,0.05243142,0.8196667,0.00003785604],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04341129,0.0004594362,0.07520207,0.003427115,0.000360981,0.0001433704,0.02261598,0.007425008,0.8469548],"genre_scores_gemma":[0.4781813,0.0009281254,0.06918291,0.0006033029,0.0001703936,0.0003477232,0.02979747,0.007328673,0.41346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1328099,"threshold_uncertainty_score":0.4442933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079901433094557,"score_gpt":0.1942323621401837,"score_spread":0.1834333478092381,"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."}}