{"id":"W4256373905","doi":"10.1088/1757-899x/707/1/011001","title":"Preface","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mechatronics; Library science; Gratitude; Technical university; Engineering; Computer science; Electrical engineering; Psychology","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":[0.0001807872,0.0001262515,0.0001338991,0.0000878473,0.00005559677,0.0002997341,0.0001630676,0.00003737118,0.00021974],"category_scores_gemma":[0.00002067082,0.0001189299,0.000006471888,0.0001554662,0.00005872583,0.000823016,0.00005224696,0.00004633614,0.0000367686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002493094,"about_ca_system_score_gemma":0.00002949147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005173513,"about_ca_topic_score_gemma":6.494827e-7,"domain_scores_codex":[0.9992635,0.000002005943,0.0001246697,0.0001777063,0.0001704104,0.0002616646],"domain_scores_gemma":[0.9997027,0.000005889054,0.00001455089,0.000141283,0.00006644461,0.00006914384],"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":[0.000003465854,0.000001634067,0.00002308564,0.0002438811,0.000003227885,7.021374e-7,0.0002586299,0.1302985,0.8660975,0.002083107,0.0000118391,0.0009744506],"study_design_scores_gemma":[0.00009020903,0.00002662411,0.002022057,0.00005838915,0.000002896075,0.00001015639,0.00006249375,0.05699334,0.9381976,0.00005526208,0.002248951,0.0002319831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945428,0.00004481508,0.001922001,0.00002980003,0.0007586514,0.0001080319,0.000004210142,0.000296923,0.002292744],"genre_scores_gemma":[0.9984975,0.0001287765,0.001150001,0.00001028929,0.00003251529,0.00001020241,0.00000203726,0.00001444716,0.0001541653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0733052,"threshold_uncertainty_score":0.4849822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007692221979507903,"score_gpt":0.183066292219575,"score_spread":0.1753740702400671,"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."}}