{"id":"W7018664293","doi":"","title":"Dont Mess with Texas: Getting the Lone Star State to Net-Zero by 2050","year":2022,"lang":"en","type":"report","venue":"Issue Lab (Candid)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workforce; State (computer science); Product (mathematics); Resource (disambiguation); Global Leadership; Natural resource; Energy (signal processing); Face (sociological concept)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.003986706,0.001520646,0.001880901,0.0004868661,0.000807696,0.0005436068,0.001992635,0.0003540971,0.002407391],"category_scores_gemma":[0.0003949402,0.001079022,0.000266862,0.001780254,0.000288834,0.0002536877,0.001203389,0.002433541,0.002233661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00336555,"about_ca_system_score_gemma":0.002150607,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06125456,"about_ca_topic_score_gemma":0.02190475,"domain_scores_codex":[0.9890107,0.0007274097,0.00139552,0.0020126,0.004796337,0.002057442],"domain_scores_gemma":[0.9937088,0.0003847691,0.001603581,0.002830639,0.0008171157,0.0006550885],"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":[0.0004729038,0.0001824036,0.001273283,0.000340438,0.001024907,0.001018947,0.002461335,0.001085412,0.0003581068,0.00001021217,0.9878542,0.003917805],"study_design_scores_gemma":[0.001100884,0.0005531387,0.0005380347,0.000501404,0.0005512696,0.0002466704,0.0005336612,0.00003030143,0.0004195306,0.00006486977,0.9938255,0.001634763],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1827864,0.1294689,0.0006805837,0.01521803,0.01140301,0.02760902,0.080947,0.006693327,0.5451937],"genre_scores_gemma":[0.07365525,0.005331839,0.002074276,0.003702753,0.004265778,0.002655792,0.01337251,0.00581794,0.8891239],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3439302,"threshold_uncertainty_score":0.9998679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605535764832396,"score_gpt":0.2780341223887622,"score_spread":0.2619787647404382,"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."}}