{"id":"W3173320109","doi":"10.1145/3463526","title":"Unlocking the Beamforming Potential of LoRa for Long-range Multi-target Respiration Sensing","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; CHIST-ERA; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Chinese Academy of Sciences; Youth Innovation Promotion Association; Agence Nationale de la Recherche","keywords":"Beamforming; Computer science; Transmitter; Key (lock); Synchronization (alternating current); SIGNAL (programming language); Channel state information; Real-time computing; Channel (broadcasting); Electronic engineering; Wireless; Telecommunications; Engineering; Computer security","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":[0.000185157,0.0001885828,0.0002743504,0.0001523566,0.0002144852,0.00005969039,0.0006175197,0.000188651,0.000002288866],"category_scores_gemma":[0.001461894,0.0001261398,0.0001132015,0.0003702496,0.0002285464,0.0002448252,0.000495314,0.0003084803,5.584785e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005702968,"about_ca_system_score_gemma":0.00001572019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008767761,"about_ca_topic_score_gemma":0.000006338572,"domain_scores_codex":[0.9990326,0.000005959143,0.0003276438,0.0002290835,0.0001619249,0.0002427612],"domain_scores_gemma":[0.9988664,0.000152622,0.0002034067,0.000360355,0.0004074735,0.000009674918],"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":[0.0002349984,0.0001389438,0.004194479,0.001131965,0.0003154613,0.000003439802,0.001861806,0.01743151,0.8721442,0.00170269,0.0008266625,0.1000138],"study_design_scores_gemma":[0.0002792997,0.0001471718,0.000268674,0.0003769019,0.00003774297,0.00001745792,0.009477531,0.01381271,0.9703882,0.004605578,0.0004492135,0.0001395379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895381,0.001282766,0.006908905,0.0005058534,0.0002958606,0.0006402067,0.00001624671,0.0005897362,0.0002223431],"genre_scores_gemma":[0.9944524,0.0003225064,0.005018698,0.00002288913,0.00002629608,0.00007710704,0.000001339031,0.00002813995,0.00005063523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09987428,"threshold_uncertainty_score":0.5143834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225004347819988,"score_gpt":0.2413049221978834,"score_spread":0.2290548787196835,"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."}}