{"id":"W4309728516","doi":"10.3390/cleantechnol4040075","title":"Data-Driven Machine Learning Approach for Predicting the Higher Heating Value of Different Biomass Classes","year":2022,"lang":"en","type":"article","venue":"Clean Technologies","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Peter's Hospital","funders":"","keywords":"Bottleneck; Artificial neural network; Biomass (ecology); Mean squared error; Random forest; Nonlinear system; Computer science; Machine learning; Decision tree; Heat of combustion; Statistics; Environmental science; Mathematics; Artificial intelligence; Agronomy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001335176,0.00143188,0.0008436446,0.001669412,0.0002996389,0.0009052188,0.001167731,0.0009713248,0.0009342261],"category_scores_gemma":[0.002049335,0.0003740685,0.001129683,0.001142408,0.0002916491,0.0007686925,0.0004166335,0.001171719,0.0003872835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008216747,"about_ca_system_score_gemma":0.0009372655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005564653,"about_ca_topic_score_gemma":0.006054577,"domain_scores_codex":[0.9995744,0.00009123334,0.00004185165,0.0001527863,0.0001017283,0.00003802787],"domain_scores_gemma":[0.9990946,0.0004877077,0.00009202035,0.00005098906,0.0002501465,0.00002457594],"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":[0.0001397495,0.0003981425,0.012463,0.0002363593,0.000205227,0.000138032,0.00005984521,0.8490056,0.004055463,0.001477031,0.00146341,0.1303582],"study_design_scores_gemma":[0.000004157087,0.00004260138,0.0009628307,0.00000869211,0.00001244729,0.00001261471,0.00001354819,0.9965276,0.001197437,0.0008690122,0.0003400123,0.000009018957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2084612,0.001509405,0.7816051,0.0005222821,0.0001582846,0.0003433664,0.002221379,0.001938405,0.003240608],"genre_scores_gemma":[0.8830869,0.0004843932,0.1110308,0.0001601582,0.00005984273,0.0005011795,0.002777031,0.00005291312,0.001846896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005564653,"threshold_uncertainty_score":0.01106453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521649096961758,"score_gpt":0.232468413638546,"score_spread":0.2072519226689284,"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."}}