{"id":"W4323644453","doi":"10.59082/yaej8738","title":"Editorial | A Homecoming for Microscopists","year":2022,"lang":"en","type":"article","venue":"The Microscope","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Homecoming; Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Pandemic; Schedule; History; Medicine; Art history; Management; Economics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00254999,0.0001539705,0.0001951074,0.00004258509,0.001763088,0.0002859761,0.001308579,0.00003037862,0.002014756],"category_scores_gemma":[0.0002157004,0.0001163383,0.00006606475,0.0001809213,0.0001856345,0.0001044221,0.000693754,0.0001986997,0.0002150182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088561,"about_ca_system_score_gemma":0.0001219302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001411858,"about_ca_topic_score_gemma":0.000005511966,"domain_scores_codex":[0.9981968,0.0002810232,0.0002687032,0.0003943176,0.0003789671,0.0004801397],"domain_scores_gemma":[0.9989666,0.000275191,0.0001766603,0.0004780979,0.00005268322,0.000050779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008308404,0.00002160765,0.00003123353,0.00001347705,0.000001474957,4.016445e-7,0.000465611,0.0008936837,0.8346933,0.0001320076,0.1636172,0.00004693807],"study_design_scores_gemma":[0.0004982961,0.000164335,0.00005683315,0.000006799999,0.00001036659,0.00001574847,0.0001356064,0.0003046812,0.401878,0.000708615,0.5960281,0.0001925892],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8300052,0.0001485833,0.001651929,0.001032704,0.1657152,0.0007227728,0.000203992,0.0001653818,0.0003542931],"genre_scores_gemma":[0.8711837,0.000006974765,0.04195911,0.002699953,0.07492458,0.001619772,0.00005928076,0.0001566597,0.007390004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4328152,"threshold_uncertainty_score":0.9995365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008533807752373333,"score_gpt":0.2672943513466683,"score_spread":0.258760543594295,"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."}}