{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002040852,0.0004767765,0.0002651701,0.001013297,0.001629295,0.005258569,0.001020154,0.0009146948,0.009531615],"category_scores_gemma":[0.003534484,0.0002005761,0.0004042443,0.0009550629,0.001270632,0.002289381,0.00167952,0.001574248,0.0005557013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004934643,"about_ca_system_score_gemma":0.005711447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05128542,"about_ca_topic_score_gemma":0.07837836,"domain_scores_codex":[0.9980057,0.0002674747,0.00004907951,0.0001615346,0.00118194,0.0003342073],"domain_scores_gemma":[0.9990916,0.0002766114,0.00008143357,0.00004953532,0.000370796,0.0001300372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003684667,0.0004466567,0.022884,0.0009118915,0.00007101131,0.0006363893,0.001394405,0.0431821,0.002436131,0.6738083,0.09486317,0.1589973],"study_design_scores_gemma":[0.000193415,0.000604654,0.02089397,0.000987943,0.000172477,0.0004340448,0.007683029,0.0238189,0.007093352,0.2130948,0.7247982,0.0002251494],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1262155,0.002351591,0.005612629,0.007802299,0.0002051845,0.0001720516,0.001367958,0.0001701524,0.8561026],"genre_scores_gemma":[0.9042221,0.005350899,0.01152731,0.00149733,0.000127345,0.0002141987,0.002147066,0.0001083636,0.07480539],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05128542,"threshold_uncertainty_score":0.1019738,"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."}}