{"id":"W2421519556","doi":"10.1002/bbb.1659","title":"Multi‐spatial analysis of forest residue utilization for bioenergy","year":2016,"lang":"en","type":"article","venue":"Biofuels Bioproducts and Biorefining","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bioenergy; Environmental science; Biomass (ecology); Environmental economics; Scenario analysis; Renewable energy; Aviation; Energy independence; Environmental resource management; Agricultural engineering; Biofuel; Business; Waste management; Engineering; Ecology; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001476441,0.0001506433,0.0002278162,0.000436939,0.00005306883,0.0000221324,0.00008813804,0.00007881998,0.00002124981],"category_scores_gemma":[0.00006373646,0.0001019703,0.00006957643,0.0006163234,0.00008499157,0.00008576727,0.0000378628,0.00001573885,0.000001678673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001814754,"about_ca_system_score_gemma":0.000007466068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005547462,"about_ca_topic_score_gemma":0.0006065711,"domain_scores_codex":[0.99911,0.000009703196,0.000289331,0.0002855985,0.0001023815,0.0002030236],"domain_scores_gemma":[0.999531,0.00002490998,0.00007242116,0.0002240694,0.00009162979,0.00005592592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008638076,0.0001050918,0.1332865,0.0003947818,0.001262463,0.00000175705,0.0002222251,0.0008494399,0.5106667,0.01435508,0.0004921985,0.3382775],"study_design_scores_gemma":[0.002387354,0.0002852278,0.4706785,0.0001613845,0.001008315,0.000001283629,0.0001375106,0.07164989,0.3929949,0.0002642863,0.0596008,0.0008305563],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8458669,0.0009592517,0.1512731,0.0003319298,0.0004619618,0.0003274936,0.000217883,0.0002635592,0.000297972],"genre_scores_gemma":[0.9944453,0.0004554208,0.004629426,0.00002393062,0.00006104429,0.00001836597,0.0000879604,0.00002193716,0.0002566174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3374469,"threshold_uncertainty_score":0.415823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03935437161093951,"score_gpt":0.2504626935564113,"score_spread":0.2111083219454717,"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."}}