{"id":"W7133363367","doi":"10.58532/nbennurradc4","title":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN GREEN ORGANIC CHEMISTRY: ADVANCING SUSTAINABLE CATALYSIS, PROCESS OPTIMIZATION, AND MATERIAL INNOVATION","year":2025,"lang":"","type":"book-chapter","venue":"","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interpretability; Process (computing); Energy consumption; Sustainable development; Process integration; Applications of artificial intelligence; Efficient energy use; Production (economics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00389899,0.001028609,0.001258182,0.0009247367,0.0009439187,0.001399212,0.0007927894,0.0006330379,0.01613982],"category_scores_gemma":[0.002352799,0.001079005,0.00004303871,0.001354259,0.0008002589,0.001164618,0.001379738,0.0008546893,0.00002037994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000326932,"about_ca_system_score_gemma":0.0007263537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888655,"about_ca_topic_score_gemma":0.0001971342,"domain_scores_codex":[0.9933555,0.0002101139,0.002349429,0.002172435,0.000759798,0.001152715],"domain_scores_gemma":[0.9966666,0.000241253,0.001330524,0.0005766546,0.0009964413,0.0001885706],"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":[0.0008968562,0.000215885,0.001864357,0.01247726,0.00008575907,0.0002066543,0.002700895,0.4840689,0.4136242,0.07094951,0.00001298117,0.01289673],"study_design_scores_gemma":[0.0005026866,0.0004085293,0.00007944152,0.001848955,0.0002990743,0.0001729639,0.002253141,0.5973412,0.3588187,0.03339352,0.001595863,0.003285918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4905999,0.001348512,0.4391824,0.004884398,0.002691442,0.00703589,0.00029237,0.001392239,0.05257285],"genre_scores_gemma":[0.8262163,0.001342624,0.0257721,0.0002938828,0.0006169624,0.0001262008,0.0008664551,0.0002570033,0.1445084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4134103,"threshold_uncertainty_score":0.9996374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070457636363106,"score_gpt":0.2495286127291706,"score_spread":0.2388240363655396,"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."}}