{"id":"W4205925188","doi":"10.22215/etd/2021-14668","title":"Assessing Tightly Bundled, Complete &amp; Technology/ Sector-Specific Policy Mixes to Enable Technology Development &amp; Adoption to Mitigate Climate Change &amp; Support a Transition towards a Low-Carbon Energy Future in Canada","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Office of Energy Efficiency; National Aeronautics and Space Administration; Natural Resources Canada; Infrastructure Canada; Advanced Research Projects Agency; Innovation, Science and Economic Development Canada; Defense Advanced Research Projects Agency; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; Environment and Climate Change Canada; Strong; Advanced Research Projects Agency - Energy","keywords":"Climate change; Environmental economics; Technology development; Business; Carbon fibers; Environmental resource management; Natural resource economics; Environmental science; Engineering; Computer science; Economics; Ecology; Manufacturing engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.005924593,0.0006796321,0.000994575,0.002026453,0.002752943,0.004685221,0.001152131,0.001335905,0.002737812],"category_scores_gemma":[0.01729155,0.0005831445,0.001288044,0.003614464,0.001360397,0.00179967,0.003381597,0.002268849,0.000232219],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1564519,"about_ca_system_score_gemma":0.209047,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934251,"about_ca_topic_score_gemma":0.9949558,"domain_scores_codex":[0.9929798,0.0008516318,0.0002632737,0.0004432343,0.002993566,0.002468505],"domain_scores_gemma":[0.9858679,0.001686489,0.001174472,0.0003377789,0.007263592,0.003669745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001379793,0.0008650228,0.5859678,0.0006513681,0.002597055,0.0004771939,0.002597898,0.2301113,0.003048284,0.04421333,0.0379715,0.09011954],"study_design_scores_gemma":[0.0001750172,0.0004071813,0.8958994,0.0003240563,0.0007726541,0.0000501948,0.008780131,0.05384215,0.001952906,0.006712475,0.03089641,0.0001874238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9456486,0.001557564,0.002800596,0.006871349,0.00007457992,0.0005509996,0.01434947,0.00007845183,0.02806839],"genre_scores_gemma":[0.9794008,0.001017517,0.00456489,0.0006965334,0.00001656712,0.0002004095,0.005067884,0.00002795687,0.009007478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8435481,"threshold_uncertainty_score":0.9783962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07869192344136464,"score_gpt":0.2655118477786489,"score_spread":0.1868199243372843,"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."}}