{"id":"W4296668464","doi":"10.1002/cjce.24674","title":"Prediction of bio‐oil yield during pyrolysis of lignocellulosic biomass using machine learning algorithms","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Machine learning; Pyrolysis; Biomass (ecology); Mean squared error; Gradient boosting; Boosting (machine learning); Algorithm; Random forest; Artificial intelligence; Computer science; Mathematics; Engineering; Statistics; Agronomy; Waste management; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008613634,0.0005910576,0.0005831426,0.0006993981,0.0001785247,0.0005188282,0.0002648169,0.0004571788,0.0002517677],"category_scores_gemma":[0.001458554,0.0001938814,0.0006000678,0.0005410175,0.0001349733,0.0005105411,0.0001716722,0.0004000905,0.0001560686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004668832,"about_ca_system_score_gemma":0.0005288926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005710963,"about_ca_topic_score_gemma":0.004083708,"domain_scores_codex":[0.9998142,0.00005718885,0.00001456212,0.00004187063,0.00005118701,0.00002109753],"domain_scores_gemma":[0.9994479,0.0003474253,0.00005557949,0.00002821395,0.0001067279,0.00001422108],"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":[0.0001563538,0.0001830749,0.01172445,0.00007025248,0.00006769517,0.00005029248,0.00001500703,0.9212596,0.01959197,0.0002061011,0.0001711404,0.04650401],"study_design_scores_gemma":[0.00000166997,0.00001959594,0.00198577,0.000001927638,0.000003692732,0.000003421644,0.000002571164,0.9938174,0.004068991,0.00006397313,0.00002810627,0.000002910727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.821939,0.000372378,0.1761076,0.00006512249,0.00002223382,0.00004303463,0.0002024382,0.000468384,0.0007797059],"genre_scores_gemma":[0.9686715,0.0001117629,0.03064546,0.000007603838,0.000003490585,0.00002423214,0.0002282392,0.00001125387,0.0002965375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005710963,"threshold_uncertainty_score":0.01135546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098495700995671,"score_gpt":0.167099791480132,"score_spread":0.1561148344701753,"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."}}