{"id":"W4405492684","doi":"10.70792/jngr5.0.v1i1.41","title":"Optimizing Carbon Capture Supply Chains with AI-Driven Supplier Quality Management and Predictive Analytics","year":2024,"lang":"en","type":"article","venue":"Journal of Next-Generation Research 5 0","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"FuelCell Energy (Canada)","funders":"","keywords":"Supply chain; Predictive analytics; Analytics; Supply chain management; Quality (philosophy); Computer science; Business; Quality management; Risk analysis (engineering); Process management; Environmental economics; Operations management; Management system; Engineering; Data science; Marketing","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":[0.0008703136,0.0001597004,0.0002262583,0.0004957411,0.0001682768,0.0002764928,0.0001410048,0.0000921625,0.00008029212],"category_scores_gemma":[0.00001852144,0.0001215351,0.00007176462,0.0003601437,0.0001743075,0.0003865697,0.00002774813,0.0005198884,0.000002416695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001683963,"about_ca_system_score_gemma":0.00008135295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002673623,"about_ca_topic_score_gemma":0.0007830439,"domain_scores_codex":[0.9977218,0.0002374371,0.0004624094,0.0002513494,0.001014963,0.0003120606],"domain_scores_gemma":[0.9991999,0.0001010023,0.0001085405,0.0001864972,0.0002103921,0.0001937298],"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":[0.0003460877,0.0002256945,0.005167323,0.0002963429,0.001223017,0.0006207183,0.008551015,0.8053437,0.04288056,0.1278246,0.003765515,0.003755475],"study_design_scores_gemma":[0.006399429,0.003220563,0.09067027,0.001238737,0.00106737,0.0002946719,0.02511672,0.6967701,0.04181,0.004222943,0.1275262,0.001662954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745483,0.002443367,0.01034566,0.00480458,0.0002334549,0.0002917309,0.00004252969,0.00003770864,0.007252636],"genre_scores_gemma":[0.9919295,0.002262313,0.002164113,0.0001574713,0.0004627998,0.00001789517,0.00005242743,0.00003215693,0.002921337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1237607,"threshold_uncertainty_score":0.4956056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07866018130442877,"score_gpt":0.3418004119483478,"score_spread":0.263140230643919,"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."}}