{"id":"W4311990372","doi":"10.1002/adfm.202211422","title":"3D Acoustofluidics via Sub‐Wavelength Micro‐Resonators","year":2022,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Australian Research Council; Ontario Ministry of Natural Resources and Forestry; University of Melbourne; Medical Research Council; RMIT University; Australian National Fabrication Facility","keywords":"Microscale chemistry; Resonator; Materials science; Wavelength; Acoustics; Acoustic wave; Nanotechnology; Optoelectronics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001729588,0.000221139,0.0002347922,0.0001290819,0.0002788157,0.00002710532,0.0001745545,0.00009023179,0.001043803],"category_scores_gemma":[0.00002665514,0.000230263,0.00005088576,0.0002129124,0.00006520315,0.0001078938,0.000169916,0.0001826825,0.000105682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967801,"about_ca_system_score_gemma":0.00002340051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005118053,"about_ca_topic_score_gemma":4.013389e-7,"domain_scores_codex":[0.9988239,0.00003399124,0.0002991405,0.0002622202,0.0002458734,0.0003348323],"domain_scores_gemma":[0.9995597,0.00004630716,0.00005087025,0.0002609587,0.00003991566,0.00004222295],"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":[0.00005399345,0.00002182944,0.00001557322,0.00002374871,0.00002829546,0.00001251434,0.0000154538,0.001483003,0.9751165,0.0003562473,0.01368647,0.009186363],"study_design_scores_gemma":[0.0003117487,0.00006045461,0.0002914968,0.000007028617,0.0000142888,0.00008717767,0.00008085955,0.0001129994,0.9146913,0.001376793,0.08268052,0.0002852766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851286,0.0014785,0.007483027,0.0001046337,0.003758752,0.000189996,0.000235376,0.001418823,0.0002022388],"genre_scores_gemma":[0.9961054,0.0004582292,0.002625903,0.0001776752,0.0001515758,0.00006985019,0.000158124,0.00005905574,0.0001941527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06899405,"threshold_uncertainty_score":0.9998694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007957553242258532,"score_gpt":0.181052492729595,"score_spread":0.1730949394873365,"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."}}