{"id":"W2769873246","doi":"10.1039/c7lc00970d","title":"Multi-size spheroid formation using microfluidic funnels","year":2017,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Mitacs; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Spheroid; Microfluidics; Nanotechnology; Chemistry; Funnel; Biophysics; Materials science; Biology; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.00009622428,0.0001625129,0.0001526479,0.00004248593,0.0002662068,0.0001208032,0.0002871464,0.000150874,0.00004290685],"category_scores_gemma":[0.0001055906,0.0001464638,0.00005300147,0.00004459139,0.00007076468,0.0001823384,0.00007012268,0.0001698375,0.0001946729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006775623,"about_ca_system_score_gemma":0.000007604621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002953989,"about_ca_topic_score_gemma":0.000006096816,"domain_scores_codex":[0.9993386,0.000009904814,0.000163002,0.0001424506,0.00008801121,0.000258014],"domain_scores_gemma":[0.9993001,0.0000221444,0.00005788833,0.000563464,0.00002050213,0.00003593089],"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":[0.000006707894,0.00001496463,0.0002375751,0.00003804545,0.00001130581,0.000008071632,0.00009221055,0.00002110683,0.984771,0.00006453086,0.007403675,0.007330816],"study_design_scores_gemma":[0.0005779931,0.00002746578,0.004310043,0.0001222372,0.00001388061,0.00002440156,0.00008782062,0.007601248,0.9748284,0.0001597266,0.0119786,0.0002682522],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884529,0.001331361,0.007252009,0.0001404156,0.0004349568,0.0001330544,0.0000123528,0.000797909,0.001445036],"genre_scores_gemma":[0.9920664,0.000403584,0.007036733,0.00008291574,0.0000748934,0.000002194303,0.000003414466,0.00002950285,0.0003003589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009942655,"threshold_uncertainty_score":0.597262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04194483469581206,"score_gpt":0.2563831183997801,"score_spread":0.2144382837039681,"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."}}