{"id":"W4256549752","doi":"10.1088/1757-899x/855/1/011001","title":"Preface","year":2020,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Session (web analytics); Library science; China; Plenary session; Engineering ethics; Work (physics); Political science; Engineering; Computer science; Medicine; Mechanical engineering; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001809756,0.0001943329,0.0002399251,0.00006287352,0.0001013257,0.000441965,0.0002000688,0.00005027552,0.00008645734],"category_scores_gemma":[0.0001023648,0.000186145,0.00001205495,0.0002630514,0.0001653712,0.0006177251,0.00007572901,0.00005632888,0.00003468278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002326966,"about_ca_system_score_gemma":0.00003839148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007123684,"about_ca_topic_score_gemma":7.224027e-7,"domain_scores_codex":[0.9989197,0.000006140452,0.0002125513,0.0002518074,0.0002183612,0.0003914717],"domain_scores_gemma":[0.9995323,0.000009177546,0.00001979171,0.0001339821,0.00008788049,0.0002169096],"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":[0.000005799064,0.000001084183,0.000003457994,0.0000931769,0.000003684271,0.000005756731,0.0005311447,0.002867286,0.9951294,0.00100253,0.00005332912,0.0003033516],"study_design_scores_gemma":[0.0000953785,0.00004673223,0.0008852228,0.0000326663,0.000005396673,0.0000247022,0.0001138354,0.01130972,0.9842438,0.00002189367,0.002962117,0.0002585298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972686,0.00004677292,0.0003256099,0.0003555485,0.0007264702,0.0001002848,0.00001387566,0.0005629922,0.0005998782],"genre_scores_gemma":[0.9981625,0.0001107888,0.001482042,0.00007021134,0.0001289787,0.000004864308,0.000001980185,0.00002463561,0.00001396749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01088559,"threshold_uncertainty_score":0.7590773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660191091968348,"score_gpt":0.185699230742557,"score_spread":0.1690973198228735,"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."}}