{"id":"W4413052290","doi":"10.1039/d5lc00514k","title":"High-speed cell partitioning through reactive machine learning-guided inkjet printing","year":2025,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Spinal Cord Injury BC; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; BC Cancer Foundation; Genome British Columbia; Michael Smith Health Research BC; Canada Foundation for Innovation","keywords":"Computer science; Workflow; Classifier (UML); Artificial intelligence; Computational biology; Biology; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006603417,0.0007378116,0.0006691081,0.0005885894,0.0004140899,0.00101177,0.0007949049,0.0005836074,0.002282654],"category_scores_gemma":[0.001123861,0.000352045,0.000412951,0.0005255385,0.0004892442,0.0003815226,0.0007180342,0.001006076,0.00221195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003412737,"about_ca_system_score_gemma":0.0003669399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006374955,"about_ca_topic_score_gemma":0.001384978,"domain_scores_codex":[0.9993054,0.00005343307,0.00005723857,0.0002319314,0.0002751964,0.00007689551],"domain_scores_gemma":[0.9991105,0.0004343366,0.00008246234,0.0002086149,0.0001316001,0.00003244998],"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":[0.00005861083,0.0000397948,0.000267489,0.00007569673,0.00001102816,0.00007465226,0.0001038637,0.001979053,0.9609468,0.0005902586,0.0005196243,0.03533311],"study_design_scores_gemma":[0.0000079552,0.0000446407,0.0005964713,0.000005639016,0.000007551496,0.00009179536,0.00001691948,0.0294618,0.965883,0.000386599,0.003477847,0.00001978429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1644928,0.0005397205,0.8206702,0.0002190427,0.0002619505,0.0002671856,0.0006863344,0.007079235,0.005783587],"genre_scores_gemma":[0.4807881,0.0005424561,0.5062088,0.0002280978,0.00003684956,0.0006049247,0.001159169,0.001052711,0.009378845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002282654,"threshold_uncertainty_score":0.007636249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613873685695833,"score_gpt":0.2495141897293248,"score_spread":0.2333754528723665,"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."}}