{"id":"W4248304314","doi":"10.1515/energyo.0033.00088","title":"Graphitic Carbon Nitride-Titanium Dioxide Nanocomposite for Photocatalytic Hydrogen Production under Visible Light","year":2018,"lang":"en","type":"dataset","venue":"energyo","topic":"Advanced Photocatalysis Techniques","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"SiliCycle (Canada); Université Laval","funders":"","keywords":"Photocatalysis; Nanocomposite; Titanium dioxide; Hydrogen production; Visible spectrum; Graphitic carbon nitride; Materials science; Carbon dioxide; Carbon nitride; Hydrogen; Chemical engineering; Nanotechnology; Chemistry; Composite material; Catalysis; Optoelectronics; Organic chemistry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003700919,0.001050766,0.001119338,0.0009276789,0.000296498,0.00007729224,0.001126128,0.0008150974,0.00009854577],"category_scores_gemma":[0.0001508584,0.001056774,0.0005667378,0.001028206,0.0002253693,0.0002591603,0.0002814607,0.0004797892,0.0001014386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004889168,"about_ca_system_score_gemma":0.0001692054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004397384,"about_ca_topic_score_gemma":0.004404034,"domain_scores_codex":[0.9955698,0.00009427686,0.0009892477,0.001722921,0.0006519459,0.0009717571],"domain_scores_gemma":[0.9958384,0.0001336626,0.000672397,0.002776143,0.0003328873,0.0002465298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001697422,0.0002174132,0.000001396933,0.0002753914,0.0005039545,0.0000175642,0.00001456303,0.001101951,0.142166,0.0003367234,0.8551189,0.00007641099],"study_design_scores_gemma":[0.0002350399,0.0001433705,7.850046e-7,0.0001405315,0.0003703484,0.00001985053,0.000005838203,0.00003357058,0.483193,0.003731126,0.5114782,0.0006483497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00435321,0.001232371,0.0006422727,0.0002274137,0.002583107,0.004221598,0.9836707,0.001581002,0.001488334],"genre_scores_gemma":[0.003129976,0.0003618631,0.001313393,0.0002663556,0.0021394,0.003052417,0.9869151,0.0002963265,0.002525212],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3436407,"threshold_uncertainty_score":0.9991882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079068526861305,"score_gpt":0.2605124409631053,"score_spread":0.2497217556944923,"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."}}