{"id":"W4413838757","doi":"10.24908/iqurcp19792","title":"Microfluidics and Chip Development","year":2025,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"3D IC and TSV technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Microfluidics; Microfluidic chip; Chip; Organ-on-a-chip; Nanotechnology; Computer science; Biochemical engineering; Materials science; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002343631,0.001141634,0.001151842,0.001721629,0.0009924127,0.002406797,0.002128531,0.001350138,0.01655092],"category_scores_gemma":[0.00309235,0.001071075,0.0009001778,0.001068132,0.000723746,0.001261924,0.001802585,0.00210483,0.01133162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182859,"about_ca_system_score_gemma":0.001938798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001282454,"about_ca_topic_score_gemma":0.00179302,"domain_scores_codex":[0.9961662,0.000401795,0.0002427574,0.0007747716,0.002134574,0.0002799463],"domain_scores_gemma":[0.9988207,0.0002717137,0.00007654374,0.0002026304,0.0004955597,0.0001328391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001931535,0.0002125238,0.001194142,0.002649083,0.0001222429,0.0005014459,0.000505621,0.003858529,0.344395,0.04382205,0.1358442,0.466702],"study_design_scores_gemma":[0.00005474687,0.0002962587,0.0008999842,0.000186537,0.00003345569,0.0007346907,0.00005996511,0.006122706,0.1731846,0.00381951,0.8144934,0.0001142519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01484221,0.03417143,0.8160015,0.005237526,0.00975839,0.004062444,0.007499924,0.0181034,0.09032314],"genre_scores_gemma":[0.07799859,0.02374046,0.7981604,0.005087631,0.001298744,0.009638087,0.006780707,0.001551552,0.07574388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01655092,"threshold_uncertainty_score":0.05536836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06113939030266621,"score_gpt":0.3276189985215977,"score_spread":0.2664796082189315,"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."}}