{"id":"W1680785731","doi":"10.5296/emsd.v4i2.8182","title":"Efficiency and Environmental Metrics of Algal Fuel","year":2015,"lang":"en","type":"article","venue":"Environmental Management and Sustainable Development","topic":"Algal biology and biofuel production","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Subsidy; Greenhouse gas; Fossil fuel; Renewable energy; Biofuel; Biomass (ecology); Environmental science; Entitlement (fair division); Natural resource economics; Fuel efficiency; Renewable fuels; Economics; Waste management; Ecology; Engineering; Biology; Automotive engineering","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":[0.001142518,0.0004491932,0.0003458895,0.002495448,0.0002967021,0.001273347,0.0003259155,0.0003734432,0.002287098],"category_scores_gemma":[0.003221568,0.0001251232,0.0003046177,0.00237938,0.0004928149,0.001588143,0.0007593387,0.0001965745,0.0005846491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009464501,"about_ca_system_score_gemma":0.00027734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001773565,"about_ca_topic_score_gemma":0.001635369,"domain_scores_codex":[0.9989825,0.0002139367,0.0000783055,0.0001850509,0.0004112426,0.0001289753],"domain_scores_gemma":[0.9984289,0.0005407875,0.0002951362,0.0001789201,0.0004835507,0.00007260551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002182576,0.0001902946,0.1235142,0.0004164183,0.0002354872,0.0002646603,0.0003590216,0.4238073,0.03909188,0.1981895,0.005101692,0.2086113],"study_design_scores_gemma":[0.00001011678,0.0004723867,0.2133576,0.00009678313,0.00009912194,0.0009882199,0.0007654924,0.604115,0.0564716,0.08430066,0.03921038,0.0001127231],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7071654,0.002958132,0.2024143,0.0004155081,0.00006441343,0.0001059876,0.002845176,0.0004051437,0.08362595],"genre_scores_gemma":[0.9817356,0.0004937387,0.01136924,0.00001983202,0.0000144393,0.00003671628,0.001241564,0.00007481196,0.005014102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002495448,"threshold_uncertainty_score":0.007651091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007436809005079336,"score_gpt":0.1787961378269089,"score_spread":0.1713593288218296,"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."}}