{"id":"W2794816098","doi":"10.1016/j.jcis.2018.03.092","title":"Biomass based iron and nitrogen co-doped 3D porous carbon as an efficient oxygen reduction catalyst","year":2018,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Waterloo; National Natural Science Foundation of China","keywords":"Catalysis; Pyrolysis; Carbon fibers; Ferrous; Methanol; Nitrogen; Inorganic chemistry; Chemical engineering; Oxygen; Chemistry; Porosity; Metal; Graphitic carbon nitride; Materials science; Organic chemistry; Photocatalysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001049332,0.0001642524,0.0002243046,0.0004125964,0.0003008108,0.0001334551,0.0003508159,0.00006669516,0.00002230751],"category_scores_gemma":[0.0001084539,0.0001337624,0.00003504896,0.0005690796,0.0007755235,0.0004522097,0.00006891065,0.0001424034,0.000005649032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002635492,"about_ca_system_score_gemma":0.0003682282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007475357,"about_ca_topic_score_gemma":0.0001115277,"domain_scores_codex":[0.9983001,0.00006320361,0.0003500768,0.0003242833,0.0006412893,0.0003210948],"domain_scores_gemma":[0.9986458,0.00002003018,0.0003262859,0.0002261279,0.0005096247,0.0002721571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002486691,0.00006118751,0.0001604852,0.000008401334,0.00001289289,0.000002918437,0.0004756686,0.0003564419,0.9976191,0.00007858609,0.0000478784,0.0009277599],"study_design_scores_gemma":[0.0006666707,0.001855544,0.0002605052,0.00004806559,0.0000391108,0.0004720017,0.0004223025,0.008752421,0.986205,0.000092946,0.001021847,0.0001635761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953997,0.0002979777,0.00009149378,0.0001715524,0.0004194475,0.00007190483,6.256572e-7,0.00001685964,0.003530422],"genre_scores_gemma":[0.999213,0.00001708116,0.0003582619,0.00004749511,0.0001682761,0.000001009708,0.00000121233,0.00001388186,0.0001797339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0114141,"threshold_uncertainty_score":0.545467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00735606391590491,"score_gpt":0.2538273215549061,"score_spread":0.2464712576390012,"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."}}