{"id":"W2075524232","doi":"10.1016/j.jala.2004.07.011","title":"High-Throughput Screening at McMaster University: Automation in Academe","year":2004,"lang":"en","type":"article","venue":"JALA Journal of the Association for Laboratory Automation","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Throughput; Automation; Doors; Engineering management; Function (biology); Process (computing); Computer science; High-throughput screening; Engineering; Manufacturing engineering; Telecommunications; Mechanical engineering; Biology; Operating system; Bioinformatics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004738023,0.00007378873,0.0001015751,0.00006013153,0.000139217,0.00001543818,0.0001365731,0.0001600559,0.00000182272],"category_scores_gemma":[0.0001974595,0.00006499694,0.00007979615,0.0002315693,0.00001553296,0.00003563781,0.00004619444,0.00009754356,0.000001380698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004425563,"about_ca_system_score_gemma":0.00007408506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002858096,"about_ca_topic_score_gemma":0.00001798916,"domain_scores_codex":[0.9993058,0.00006552696,0.0002601711,0.0001008461,0.0001607145,0.0001068936],"domain_scores_gemma":[0.9987954,0.00002260855,0.0007558712,0.0001042887,0.0002984183,0.00002343763],"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":[0.0001721063,0.0001251941,0.008952846,0.00002602213,0.0001214832,0.00000105757,0.0002649046,0.025974,0.94911,0.006919964,0.004494383,0.003838078],"study_design_scores_gemma":[0.00445404,0.0002655053,0.1323866,0.0001815948,0.0001124374,0.00001425304,0.0001901091,0.00114368,0.6877828,0.005148253,0.1679638,0.000356839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940229,0.00004339857,0.05622907,0.002953141,0.0001483484,0.0002754371,0.00004387101,0.00002565496,0.0000520953],"genre_scores_gemma":[0.9731884,0.00003024966,0.02595085,0.0002241413,0.0001418463,0.0000024396,0.00002603779,0.00001154306,0.0004244824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2613271,"threshold_uncertainty_score":0.2650498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007153721063349543,"score_gpt":0.2418516794182116,"score_spread":0.2346979583548621,"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."}}