{"id":"W2134784362","doi":"10.1140/epjti/s40485-014-0009-z","title":"Real-time gas identification on mobile platforms using a nanomechanical membrane-type surface stress sensor","year":2014,"lang":"en","type":"article","venue":"EPJ Techniques and Instrumentation","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Japan Science and Technology Agency; Research Foundation for Opto-Science and Technology; TEPCO Memorial Foundation; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Identification (biology); Embedded system; Computer science; Real-time computing; Wireless; Sensor array; Computer hardware; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0001525164,0.0004140692,0.0003326493,0.0002465716,0.0001920734,0.0003118407,0.0003975391,0.000557086,0.0009766039],"category_scores_gemma":[0.0002742381,0.0001395792,0.0001550124,0.0001804614,0.0002140283,0.0005224038,0.0003575413,0.0003262615,0.0004058648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573594,"about_ca_system_score_gemma":0.0001255309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003244439,"about_ca_topic_score_gemma":0.0006460675,"domain_scores_codex":[0.999772,0.0000204339,0.000009146148,0.00006377266,0.0001104516,0.00002407971],"domain_scores_gemma":[0.9998333,0.00005606801,0.00004165643,0.00002149945,0.00003437379,0.00001309329],"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":[0.00007922373,0.00002304159,0.001116397,0.0000379388,0.000006309268,0.0001675967,0.00003936623,0.0004703555,0.9802963,0.0001703698,0.0001795008,0.01741361],"study_design_scores_gemma":[0.00001131386,0.0003882611,0.004026445,0.000009062153,0.0000131736,0.0004559798,0.00007591043,0.0305084,0.9609588,0.0001861934,0.003339129,0.00002728809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8235064,0.000779789,0.1713857,0.0004289443,0.0002038862,0.00008967321,0.0002320921,0.001332537,0.002040955],"genre_scores_gemma":[0.8961138,0.0003114373,0.1007677,0.0001425013,0.00004397261,0.00005591949,0.0000821168,0.00003281267,0.002449706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009766039,"threshold_uncertainty_score":0.00326705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157195873369679,"score_gpt":0.2724104470645898,"score_spread":0.260838488330893,"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."}}