{"id":"W4401034084","doi":"10.1016/j.carbon.2024.119488","title":"Carbon science perspective in 2024: Current research and future challenges","year":2024,"lang":"en","type":"article","venue":"Carbon","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Vermont Agency of Natural Resources; H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Australian Research Council; Horizon 2020 Framework Programme; Agence Nationale de la Recherche; National Science Foundation","keywords":"Current (fluid); Perspective (graphical); Carbon fibers; Engineering ethics; Engineering physics; Nanotechnology; Management science; Engineering; Materials science; Computer science; Electrical engineering; Artificial intelligence; Composite material","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":[0.0004825564,0.00009641818,0.0001024084,0.0003311869,0.00002494203,0.00006493399,0.0001230256,0.00003621172,0.00001123116],"category_scores_gemma":[0.00002112295,0.00009188493,0.000005451874,0.0003960888,0.0001705579,0.0001004892,0.00008786848,0.0002683177,0.000003795213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004013528,"about_ca_system_score_gemma":0.00003183229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001601074,"about_ca_topic_score_gemma":0.00001249858,"domain_scores_codex":[0.9990273,0.00002263265,0.00009825018,0.0002993586,0.0002515221,0.0003009925],"domain_scores_gemma":[0.9996998,0.00003595885,0.000004113158,0.0001684844,0.00004789584,0.00004374122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008090334,0.000209344,0.001546661,0.004911387,0.0001053086,0.0005188709,0.06948085,0.00144001,0.3914226,0.2212209,0.0009080636,0.3081551],"study_design_scores_gemma":[0.003128255,0.001408855,0.0482203,0.007020333,0.00009515704,0.0001649663,0.075082,0.1224543,0.2378888,0.2687378,0.2306379,0.005161282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8706393,0.08880848,0.000001406931,0.0005143674,0.003834627,0.0001712923,0.000002835094,0.0001228017,0.03590489],"genre_scores_gemma":[0.9925489,0.006685257,0.00002531745,0.000002122162,0.0006279006,0.0000359029,3.206998e-7,0.00002126604,0.00005295529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3029939,"threshold_uncertainty_score":0.3746959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05403356514526359,"score_gpt":0.3622598924018084,"score_spread":0.3082263272565449,"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."}}