{"id":"W4244513069","doi":"10.1063/1.5144137.1","title":"10.1063/1.5144137.1","year":2020,"lang":"en","type":"dataset","venue":"Default Digital Object Group","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; St. Michael's Hospital","funders":"","keywords":"Polyethylene glycol; Microfluidics; Biocompatible material; PEG ratio; Materials science; Dextran; Ferrofluid; Phase (matter); Chemical engineering; Nanotechnology; Aqueous two-phase system; Scaling; Magnetic field; Chemistry; Chromatography; Biomedical engineering; Organic chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008762046,0.00272491,0.00193422,0.003996702,0.0007633294,0.003023165,0.003291692,0.002683674,0.4009419],"category_scores_gemma":[0.003375428,0.0006079426,0.001031601,0.007271997,0.0005688995,0.001354977,0.00253369,0.001608212,0.6186761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320289,"about_ca_system_score_gemma":0.001594952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009803724,"about_ca_topic_score_gemma":0.01121443,"domain_scores_codex":[0.9990607,0.0001433392,0.00008955007,0.0002896791,0.000239894,0.0001768675],"domain_scores_gemma":[0.9990735,0.0002051835,0.0000703769,0.000320392,0.0001812322,0.0001492783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007125362,0.00003209576,0.001027933,0.001411518,0.00004236108,0.00003770828,0.00002799998,0.0006030311,0.0002969051,0.0007635265,0.9771426,0.01854304],"study_design_scores_gemma":[0.0001109674,0.00002551145,0.001677784,0.0002946452,0.00001393868,0.00004674693,0.00004185431,0.0006184463,0.0004663601,0.001678249,0.9950041,0.0000212942],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001262203,0.0001227638,0.0001422131,0.00007530132,0.00003640573,0.00002719138,0.99498,0.00143271,0.003057165],"genre_scores_gemma":[0.0005634482,0.0001715266,0.0004282637,0.00007742397,0.00001226739,0.0001128344,0.9955764,0.0002080023,0.002849935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5990582,"threshold_uncertainty_score":0.8544837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00977155339217223,"score_gpt":0.2212268379321598,"score_spread":0.2114552845399876,"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."}}