{"id":"W4412438485","doi":"10.1016/j.clscn.2025.100252","title":"Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness","year":2025,"lang":"en","type":"article","venue":"Cleaner Logistics and Supply Chain","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Biofuel; Biomass (ecology); Business; Engineering; Waste management; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001263635,0.000130595,0.0001474731,0.0001142292,0.0001094147,0.00004269992,0.00006666176,0.00006660262,0.00001382981],"category_scores_gemma":[0.00003227833,0.0001300076,0.00003294097,0.0001676654,0.00005884174,0.00003256754,0.00001099934,0.00007283525,5.472517e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004158543,"about_ca_system_score_gemma":0.00000681879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002885811,"about_ca_topic_score_gemma":0.0001231637,"domain_scores_codex":[0.9993711,0.00001511116,0.0001936225,0.0001825542,0.00006370153,0.0001739465],"domain_scores_gemma":[0.9997581,0.0000548405,0.00002615957,0.00007934265,0.00004193766,0.00003965052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003196354,0.000379931,0.125818,0.008160363,0.0006304986,0.00002259838,0.002044671,0.305545,0.03266517,0.4105575,0.0007214397,0.1131352],"study_design_scores_gemma":[0.001096465,0.00006059661,0.005807636,0.00007125058,0.00005572662,5.294047e-7,0.0007297026,0.9689735,0.00425518,0.0003282002,0.01839134,0.0002299375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0284573,0.0003404251,0.9642017,0.00005455914,0.0001843178,0.0003799777,0.000144214,0.00022299,0.006014542],"genre_scores_gemma":[0.9950118,0.0001575194,0.003131727,0.00005038589,0.00002605521,0.00007053154,0.0009046069,0.00001959691,0.0006277433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9665545,"threshold_uncertainty_score":0.5301555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009287795753609373,"score_gpt":0.2176224382679695,"score_spread":0.2083346425143601,"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."}}