{"id":"W3113715313","doi":"10.1039/d0ma00797h","title":"Boosting Li–S battery performance using an in-cell electropolymerized conductive polymer","year":2020,"lang":"en","type":"article","venue":"Materials Advances","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Electrical conductor; Battery (electricity); Materials science; Electrical engineering; Computer science; Composite material; Engineering; Artificial intelligence; Physics; Power (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001332376,0.0003556566,0.0005184378,0.00008580654,0.00008755347,0.0001044762,0.0002696386,0.0000760238,0.0008132],"category_scores_gemma":[0.00001961584,0.000368972,0.00002049409,0.0001936035,0.00007221406,0.001513026,0.00007299084,0.0001106287,0.0000602569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008491286,"about_ca_system_score_gemma":0.00001906292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001187447,"about_ca_topic_score_gemma":0.000001160255,"domain_scores_codex":[0.9981538,0.00009342811,0.000589519,0.0004024688,0.0001820548,0.0005786955],"domain_scores_gemma":[0.9994761,0.00003445056,0.0001319561,0.0002262322,0.00002216773,0.0001091462],"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":[0.000115411,0.00002394759,0.0002573673,0.0002486674,0.000009495729,0.00001062615,0.0003283838,0.01110556,0.9873962,0.000008082176,0.000009529653,0.0004866903],"study_design_scores_gemma":[0.000591209,0.0001160482,0.00008056666,0.00006094495,0.00001293801,0.000007710416,0.000177146,0.002331166,0.9958418,0.00003801813,0.0003073341,0.0004350935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950018,0.001245606,0.001540946,0.00004503224,0.001212194,0.0002783477,0.00004991992,0.0003077313,0.0003184447],"genre_scores_gemma":[0.9958065,0.0002079595,0.002933034,0.0004613602,0.0004025715,0.00004098829,0.00002992858,0.00010259,0.00001509223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00877439,"threshold_uncertainty_score":0.9998762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540154919247867,"score_gpt":0.2506908001922785,"score_spread":0.2252892509997998,"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."}}