{"id":"W4396510331","doi":"10.1111/gcbb.13144","title":"Bio‐innovation for environmental sustainability: Asymmetric nexus between bioenergy technology budgets and ecological footprint","year":2024,"lang":"en","type":"article","venue":"GCB Bioenergy","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nexus (standard); Bioenergy; Ecological footprint; Sustainability; Natural resource economics; Environmental resource management; Business; Environmental economics; Environmental impact assessment; Sustainable development; Economics; Ecology; Renewable energy; Engineering; Biology","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"],"consensus_categories":[],"category_scores_codex":[0.000545547,0.0003710428,0.0003453386,0.0004726457,0.0002708986,0.0001054813,0.0002990615,0.0003915872,0.0002641322],"category_scores_gemma":[0.0002391103,0.0003221169,0.0001161507,0.001381391,0.001072418,0.0002252365,0.0007062597,0.0002318795,0.00004323389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001530346,"about_ca_system_score_gemma":0.00003213734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001516876,"about_ca_topic_score_gemma":0.0000406792,"domain_scores_codex":[0.9973758,0.00007315494,0.0005122001,0.0009818125,0.0002882796,0.0007687489],"domain_scores_gemma":[0.9991145,0.0001791237,0.00008592658,0.0004341879,0.0000107368,0.0001755864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004513476,0.0003849883,0.2705203,0.0001132674,0.00008286131,0.00004138279,0.0001298634,0.0003094022,0.01035059,0.01779581,0.0003808728,0.6998455],"study_design_scores_gemma":[0.0009584517,0.00187982,0.6374435,0.00001656813,0.0001200294,0.00005357182,0.001583406,0.002330549,0.03180579,0.09133156,0.2313883,0.001088526],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902833,0.0004655508,0.002787229,0.003749858,0.0001806186,0.0006272898,0.00005526597,0.0002400571,0.001610901],"genre_scores_gemma":[0.9969598,0.00008017711,0.0008730387,0.0001699247,0.0001050708,0.0001927504,0.00009233113,0.00004042524,0.001486488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.698757,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009058948394329512,"score_gpt":0.255876587852273,"score_spread":0.2468176394579435,"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."}}