{"id":"W4378902706","doi":"10.18331/brj2023.10.2.4","title":"Machine learning in biohydrogen production: a review","year":2023,"lang":"en","type":"review","venue":"Biofuel Research Journal","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biohydrogen; Machine learning; Artificial intelligence; Computer science; Production (economics); Biochemical engineering; Engineering; Hydrogen production; Chemistry; Hydrogen","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01778695,0.0006572737,0.002796693,0.002954474,0.0005500693,0.0003003453,0.001499256,0.0004813469,0.0006058799],"category_scores_gemma":[0.006815577,0.0004883413,0.0008793833,0.006207378,0.0001946838,0.0002230986,0.0005199275,0.005847195,0.003430334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001429093,"about_ca_system_score_gemma":0.002464418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816582,"about_ca_topic_score_gemma":0.0009855484,"domain_scores_codex":[0.9857154,0.007128098,0.002198478,0.0009529344,0.002352447,0.001652681],"domain_scores_gemma":[0.9966776,0.0005770098,0.0007354615,0.0008897544,0.0005414598,0.0005786666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005729662,0.00005836229,0.000009528165,0.08092474,0.000289819,0.001534883,0.00001972467,0.0001095271,0.000002413144,0.0001366688,0.007665841,0.9092427],"study_design_scores_gemma":[0.0001317327,0.00006448395,2.899045e-7,0.1739321,0.0000902923,0.002814816,0.00001450548,0.00002125741,0.000001973233,0.0001826088,0.8223752,0.000370743],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001608437,0.9921456,0.000001770888,0.00045932,0.001319411,0.001020093,0.00001196938,0.0001821898,0.004858043],"genre_scores_gemma":[0.000001616019,0.9562166,0.00004569109,0.00001590456,0.003463399,0.000361623,0.0001133157,0.0002887671,0.03949306],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.908872,"threshold_uncertainty_score":0.9997568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2561403402724642,"score_gpt":0.4483001052308915,"score_spread":0.1921597649584272,"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."}}