{"id":"W7054943215","doi":"","title":"Assessment of the potentials to increase emissions reduction targets by the major GHGs emitters taking into consideration technological and political feasibility","year":2019,"lang":"en","type":"report","venue":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","topic":"Advanced Frequency and Time Standards","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reduction (mathematics); Greenhouse gas; Distribution (mathematics); Politics; Quantitative assessment","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":[0.002883664,0.0004658406,0.0002345253,0.001192656,0.0007503161,0.001754116,0.0003911168,0.000547365,0.003491402],"category_scores_gemma":[0.003766097,0.0001753663,0.0005747454,0.001355009,0.0003699635,0.0008740054,0.001179966,0.0005502208,0.0004993146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767381,"about_ca_system_score_gemma":0.003000415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009174888,"about_ca_topic_score_gemma":0.01990211,"domain_scores_codex":[0.9979144,0.0005007757,0.0000487951,0.0001161805,0.00108334,0.0003365077],"domain_scores_gemma":[0.9977835,0.0008011479,0.0002322133,0.00009172963,0.001012808,0.00007871368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001648447,0.0003647024,0.1603867,0.001797269,0.0006246807,0.001785627,0.001523155,0.2368681,0.05858679,0.1347577,0.007881612,0.3937752],"study_design_scores_gemma":[0.0001680255,0.002709057,0.5245517,0.000639284,0.001262792,0.001370977,0.01482866,0.0771491,0.1031827,0.06464415,0.2093161,0.000177463],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6575288,0.002645466,0.02291973,0.0022654,0.00006446995,0.0005247397,0.002214147,0.0001130981,0.3117243],"genre_scores_gemma":[0.9816232,0.00129058,0.006897642,0.00007400364,0.00001478325,0.0001503079,0.0005890904,0.00002045401,0.009339992],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009174888,"threshold_uncertainty_score":0.01824296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224168886691613,"score_gpt":0.3378535609428044,"score_spread":0.3154366722736431,"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."}}