{"id":"W2305616423","doi":"10.1021/acs.macromol.5b02617","title":"Continuous Confinement Fluidics: Getting Lots of Molecules into Small Spaces with High Fidelity","year":2016,"lang":"en","type":"article","venue":"Macromolecules","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Fluidics; Nanotechnology; Fidelity; High fidelity; Molecule; Chemistry; Materials science; Computer science; Engineering; Aerospace engineering; Telecommunications; Organic chemistry; Electrical engineering","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":[0.0002780933,0.0002816254,0.0001833872,0.0001891169,0.0003118659,0.0006208411,0.0003754779,0.0003627597,0.0005193356],"category_scores_gemma":[0.0005058016,0.0001635101,0.000147628,0.0001608627,0.0009422909,0.000877753,0.0005349001,0.0003764462,0.0002342805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004522947,"about_ca_system_score_gemma":0.000358209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005882339,"about_ca_topic_score_gemma":0.0007408582,"domain_scores_codex":[0.9998451,0.00002475434,0.000007302555,0.00004098099,0.00005702054,0.00002488075],"domain_scores_gemma":[0.9997675,0.0001013279,0.00006065364,0.00002816243,0.00002201939,0.00002032524],"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":[0.00003398005,0.00001387712,0.0002483785,0.00009030288,0.000007064965,0.0000369292,0.00007309931,0.001167394,0.9847872,0.002532278,0.0001575752,0.01085202],"study_design_scores_gemma":[0.00001150165,0.00009300024,0.0003326018,0.000009340826,0.000008557672,0.00007997053,0.00002337322,0.006980645,0.9837812,0.0006570018,0.00800896,0.00001379637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7436738,0.007536658,0.2423902,0.0008342848,0.0001352474,0.00006954691,0.0002098607,0.0006483119,0.004502061],"genre_scores_gemma":[0.945345,0.002299387,0.0495343,0.0001436419,0.00005240154,0.00007949161,0.0001278244,0.00007678098,0.002341117],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0006208411,"threshold_uncertainty_score":0.003281653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007194219447275794,"score_gpt":0.2068370771076714,"score_spread":0.1996428576603956,"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."}}