{"id":"W4381889747","doi":"10.1007/s11831-023-09950-9","title":"An Analytical Review on the Utilization of Machine Learning in the Biomass Raw Materials, Their Evaluation, Storage, and Transportation","year":2023,"lang":"en","type":"article","venue":"Archives of Computational Methods in Engineering","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Biomass (ecology); Raw material; Production (economics); Work (physics); Computer science; Process engineering; Machine learning; Engineering; Mechanical engineering; Chemistry","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":[0.001572757,0.00009359208,0.0001533077,0.0002902489,0.00001875602,0.000008901695,0.0001149775,0.00001841002,0.000009683147],"category_scores_gemma":[0.0001603694,0.00006584969,0.00002385412,0.0005932064,0.00003236304,0.0000542712,0.000007357722,0.00007432928,2.810179e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008919626,"about_ca_system_score_gemma":0.000007752652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005679201,"about_ca_topic_score_gemma":0.000009084459,"domain_scores_codex":[0.9989599,0.0003274751,0.0003411398,0.0001007984,0.0001838798,0.00008678004],"domain_scores_gemma":[0.998958,0.000858179,0.00004869965,0.0001005746,0.00002150754,0.00001306566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005321873,0.00001492228,0.0003394181,0.0005308759,0.00001455677,4.95238e-7,0.0007947176,0.969461,0.00254144,0.01265995,0.000007080473,0.01363024],"study_design_scores_gemma":[0.0001421374,0.00002232418,0.1258501,0.0003073515,0.00001144233,3.728167e-7,0.00008911001,0.8701572,0.001234388,0.002056407,0.00007552956,0.00005364079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3440626,0.001381505,0.6532722,0.0002746171,0.000101719,0.0006292218,0.00002007564,0.00008875052,0.0001692835],"genre_scores_gemma":[0.9876338,0.0007071766,0.01139045,0.00002418787,0.000007320781,0.00003419832,0.0001883258,0.00001324391,0.000001275512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6435712,"threshold_uncertainty_score":0.2685273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05752529173521408,"score_gpt":0.358561595561723,"score_spread":0.3010363038265089,"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."}}