{"id":"W4387349118","doi":"10.1039/d3dd00115f","title":"Digital pipette: open hardware for liquid transfer in self-driving laboratories","year":2023,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipette; Grippers; Robot; Computer science; Transfer (computing); Computer hardware; Artificial intelligence; Engineering; Mechanical engineering; Chemistry; Operating system","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00006769107,0.0001990074,0.0002307541,0.00009839219,0.00004717565,0.001733445,0.000382908,0.00006678751,0.000009152054],"category_scores_gemma":[0.00006234619,0.0001925072,0.00008069903,0.0004669054,0.00002330831,0.003942413,0.0001067534,0.0001002984,0.00008768692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005427549,"about_ca_system_score_gemma":0.0000382002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003320758,"about_ca_topic_score_gemma":0.00002394446,"domain_scores_codex":[0.9989403,0.000004029957,0.0002644921,0.0002620658,0.0001450309,0.0003840275],"domain_scores_gemma":[0.9995895,0.00007979825,0.000009318678,0.0002211096,0.000032875,0.0000674089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00132588,0.001845164,0.08120215,0.005159404,0.001772106,0.001356229,0.02123069,0.3966816,0.01396559,0.140347,0.1138182,0.221296],"study_design_scores_gemma":[0.00440791,0.001477129,0.01818324,0.00172614,0.00009081781,0.00003844951,0.00612982,0.07115839,0.08909971,0.008419299,0.7925883,0.00668079],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9205062,0.0001531873,0.04971417,0.0001290821,0.0008733527,0.0008798326,0.001206174,0.0009163048,0.0256217],"genre_scores_gemma":[0.9976757,0.00004600589,0.00004308521,0.00003395247,0.00008434724,0.0001135984,0.0002593745,0.00006587645,0.001678083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6787701,"threshold_uncertainty_score":0.9993029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522944292266276,"score_gpt":0.2403597986798576,"score_spread":0.2251303557571948,"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."}}