{"id":"W4321458914","doi":"10.1080/23080477.2023.2176749","title":"Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning","year":2023,"lang":"en","type":"article","venue":"Smart Science","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"École Nationale d'Administration Publique; Université du Québec à Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Biomass (ecology); Renewable energy; Environmental science; Environmental economics; Supply and demand; Resilience (materials science); Bioenergy; Environmental resource management; Business; Engineering; Economics; Ecology","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":[0.001439673,0.0001267381,0.0001093824,0.0004028962,0.0009755932,0.0001654038,0.0002191668,0.00003781793,0.0000225149],"category_scores_gemma":[0.0009248214,0.000117092,0.00002466166,0.002371963,0.0003868174,0.0003762764,0.0002679659,0.0001358534,0.00002350354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00011705,"about_ca_system_score_gemma":0.00004156501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001782759,"about_ca_topic_score_gemma":0.0005327801,"domain_scores_codex":[0.998666,0.00003411689,0.0002538994,0.0003479964,0.0003380901,0.0003598871],"domain_scores_gemma":[0.9994447,0.0001105232,0.0000356849,0.0002108888,0.00006136225,0.0001368403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006298443,0.00002621279,0.7114417,0.0001084091,0.00001816104,0.00001257541,0.004167362,0.1117464,0.1562586,0.004661021,0.0003756809,0.0111775],"study_design_scores_gemma":[0.0000983752,0.00001840852,0.054094,0.00006092426,0.000006008352,0.000004277117,0.0007372696,0.9418117,0.001844897,0.000271243,0.0009314027,0.0001214364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343301,0.00009686137,0.06319932,0.0001049647,0.0004098067,0.0001853257,0.000004575773,0.0006261938,0.001042818],"genre_scores_gemma":[0.9953808,0.00005874671,0.004355811,0.0000134613,0.00002206271,0.000009971881,0.000007814897,0.00001313019,0.0001382309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8300653,"threshold_uncertainty_score":0.7503573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630807425225535,"score_gpt":0.2604507927583897,"score_spread":0.2441427185061344,"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."}}