{"id":"W3159822725","doi":"10.1063/5.0039366","title":"Transforming a well into a chip: A modular 3D-printed microfluidic chip","year":2021,"lang":"en","type":"article","venue":"APL Bioengineering","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; Israel Science Foundation; H2020 European Research Council; Teva Pharmaceutical Industries","keywords":"Modular design; Microfluidics; Chip; Organ-on-a-chip; Lab-on-a-chip; Computer science; Embedded system; Microfluidic chip; Nanotechnology; Computer hardware; Materials science; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003433502,0.0003451605,0.0003456351,0.0002839497,0.00009105517,0.00009768821,0.0004060453,0.0002112313,0.0002865231],"category_scores_gemma":[0.0001924586,0.0003817815,0.0001580064,0.0009156745,0.00006838737,0.0001502597,0.0001443873,0.0006805744,0.0002920337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002428279,"about_ca_system_score_gemma":0.00009048465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003712381,"about_ca_topic_score_gemma":0.000006804563,"domain_scores_codex":[0.9978158,0.00002822806,0.0004283166,0.0004243554,0.000451399,0.0008518655],"domain_scores_gemma":[0.9989334,0.0001135202,0.00001670661,0.0005265091,0.00007461218,0.0003352748],"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":[0.000004331176,0.00003017032,0.000134195,0.0005680796,0.0001321972,0.0001701287,0.0005859953,0.003013005,0.9774017,0.0002398164,0.000248349,0.01747202],"study_design_scores_gemma":[0.0005674304,0.00002841356,0.0004762441,0.0003073706,0.00002388107,0.0000909928,0.00008643087,0.1362283,0.7819998,0.0001343721,0.07950143,0.0005553451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8073786,0.007786744,0.1795646,0.0003233531,0.0006961682,0.0002724717,0.000006970885,0.001326831,0.002644256],"genre_scores_gemma":[0.9868624,0.0009521907,0.01142946,0.0000540546,0.0002233857,0.00005382205,0.0000286005,0.0001340749,0.0002619706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1954019,"threshold_uncertainty_score":0.9998634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039322868288878,"score_gpt":0.2377107433256919,"score_spread":0.2273175146428032,"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."}}