{"id":"W3004973099","doi":"10.2172/1597217","title":"Getting to Neutral: Options for Negative Carbon Emissions in California","year":2020,"lang":"en","type":"report","venue":"","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Carbon neutrality; Scope (computer science); Incentive; Environmental science; Carbon dioxide; Carbon dioxide in Earth's atmosphere; Neutrality; Biomass (ecology); Environmental economics; Greenhouse gas; Carbon fibers; Natural resource economics; Environmental resource management; Computer science; Economics; Political science; Chemistry; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001739881,0.0003902595,0.0006026687,0.0004307608,0.00001597099,0.00003344731,0.000300192,0.0005632575,0.00001794455],"category_scores_gemma":[0.001527899,0.0003851806,0.000178679,0.0004375995,0.0000243282,0.0000282224,0.0001363043,0.0007424076,0.00001178781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007389266,"about_ca_system_score_gemma":0.0002180373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003964019,"about_ca_topic_score_gemma":0.001742536,"domain_scores_codex":[0.9983932,0.00000962509,0.0005184969,0.0004175313,0.0002277548,0.0004333495],"domain_scores_gemma":[0.9990673,0.0002023607,0.00005979653,0.0003895331,0.0001289622,0.0001520384],"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":[0.00004730964,0.0000961398,0.001202246,0.00439842,0.0008331121,0.0002620439,0.00216127,0.2797056,0.06542041,0.002503848,0.6246504,0.01871919],"study_design_scores_gemma":[0.0009821987,0.0001718995,0.001770986,0.001808232,0.0003108501,0.00007687798,0.001878491,0.05100339,0.05206339,0.003633246,0.8828596,0.00344083],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0545635,0.004742851,0.01229734,0.01192227,0.00526859,0.00982287,0.002360373,0.0233202,0.875702],"genre_scores_gemma":[0.9223931,0.001636733,0.06373943,0.0003872181,0.001468576,0.003571096,0.0003700649,0.0008106736,0.005623048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.870079,"threshold_uncertainty_score":0.99986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255664464980373,"score_gpt":0.2796355933164255,"score_spread":0.2470789486666217,"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."}}