{"id":"W4402380869","doi":"10.1101/2024.09.04.611131","title":"Machine Learning Driven Optimization for High Precision Cellular Droplet Bioprinting","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Nanotechnology; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008783,0.0007668406,0.0008261065,0.0003803948,0.0002066328,0.0006009329,0.0005617614,0.000931458,0.001198917],"category_scores_gemma":[0.002040326,0.0004985729,0.0004102898,0.0003434995,0.0005697026,0.0004755705,0.000533762,0.0008113956,0.0001885745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113102,"about_ca_system_score_gemma":0.0009815651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005237448,"about_ca_topic_score_gemma":0.003285289,"domain_scores_codex":[0.9997759,0.00005932894,0.000009926536,0.00005666679,0.00005891981,0.00003932826],"domain_scores_gemma":[0.9989477,0.0007310446,0.00009149896,0.00004176375,0.0001574226,0.00003066676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001726725,0.00001951656,0.0001795569,0.00001738519,0.000006611064,0.00001040444,0.00000592356,0.9935251,0.001226761,0.0004732692,0.0001182785,0.004399798],"study_design_scores_gemma":[0.000002089594,0.000005656886,0.00002373301,6.848688e-7,5.585079e-7,7.265972e-7,6.579401e-7,0.9995537,0.0002547544,0.0001207467,0.00003591265,7.664858e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.150444,0.0005266715,0.8441855,0.0003565527,0.00004973071,0.0001119008,0.000146875,0.0005991287,0.003579761],"genre_scores_gemma":[0.8932059,0.0001338539,0.1038598,0.0001098856,0.00001683352,0.000192558,0.0001701642,0.00009216645,0.002218858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005237448,"threshold_uncertainty_score":0.01041394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172084893857561,"score_gpt":0.227611420208926,"score_spread":0.2158905712703504,"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."}}