{"id":"W4405977110","doi":"10.29173/scientia10","title":"Reading Galvanometers","year":2024,"lang":"en","type":"article","venue":"Scientia Canadensis Canadian Journal of the History of Science Technology and Medicine","topic":"History of Science and Natural History","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Galvanometer; Context (archaeology); Reading (process); Electricity; Mathematics education; Selection (genetic algorithm); Landmark; Computer science; Engineering; Engineering ethics; Political science; Electrical engineering; Psychology; History; Physics; Artificial intelligence; Optics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001453798,0.0001005584,0.0002143007,0.005219137,0.0009047413,0.00002121884,0.0008642371,0.00004953304,0.0007020933],"category_scores_gemma":[0.0003152918,0.00006338459,0.00006339776,0.001096837,0.044757,0.0004683196,0.00003703414,0.0003358055,0.00000400162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001384393,"about_ca_system_score_gemma":0.005251872,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005508293,"about_ca_topic_score_gemma":0.03259965,"domain_scores_codex":[0.9986786,0.00001719569,0.0003183954,0.0002101215,0.0004511135,0.0003245484],"domain_scores_gemma":[0.998917,0.00002889719,0.0001800081,0.0002125463,0.0003083967,0.0003530948],"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":[0.000005346972,0.000005360213,0.0001931859,0.00006136717,0.00002685733,0.0001153288,0.04833515,0.000002831145,0.02020248,0.354664,0.5650036,0.0113844],"study_design_scores_gemma":[0.00007006713,0.00009723092,0.0001022146,0.0003083475,0.00004669263,0.0001840325,0.0029657,0.000019424,0.0001359021,0.002138467,0.993854,0.00007785569],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6256503,0.1236411,0.00001643157,0.0602413,0.1120671,0.0002583991,0.00001783478,0.00006414687,0.0780433],"genre_scores_gemma":[0.9724392,0.00002638261,0.00004881864,0.0005412008,0.0001390772,4.4578e-7,8.991297e-8,0.000005318179,0.02679952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4288504,"threshold_uncertainty_score":0.9850529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873013240128271,"score_gpt":0.2120472596329544,"score_spread":0.1933171272316717,"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."}}