{"id":"W1989115430","doi":"10.1109/infocom.2014.6847958","title":"Electronic frog eye: Counting crowd using WiFi","year":2014,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":405,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scalability; Computer science; Metric (unit); Channel (broadcasting); Monotonic function; Channel state information; Code (set theory); Reliability (semiconductor); State (computer science); Artificial intelligence; Real-time computing; Wireless; Computer vision; Computer engineering; Algorithm; Computer network; Mathematics; Database; Telecommunications","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":[0.0005072469,0.0008613536,0.0007064662,0.001601629,0.000624348,0.0006252495,0.00131115,0.0007746262,0.00184406],"category_scores_gemma":[0.002668553,0.0002736421,0.0003437616,0.0009401162,0.0005011301,0.001429615,0.001897309,0.0004407878,0.00069704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005377075,"about_ca_system_score_gemma":0.0005472127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00729507,"about_ca_topic_score_gemma":0.00707087,"domain_scores_codex":[0.9994034,0.0001243493,0.00002356553,0.0001150201,0.0002240775,0.0001096004],"domain_scores_gemma":[0.999307,0.0002504272,0.00009611074,0.0001227876,0.0001564414,0.00006718723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009418403,0.0002154592,0.01617491,0.0003723992,0.0001813157,0.0007296878,0.0007604258,0.1445696,0.03697652,0.009692567,0.01481561,0.7745696],"study_design_scores_gemma":[0.00006393792,0.0002948674,0.004670519,0.00005438216,0.00005458469,0.000630869,0.0002414267,0.9603899,0.01876895,0.005924788,0.008804228,0.0001016708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1616161,0.001104651,0.8106809,0.0004451742,0.000319394,0.0002615467,0.0005195982,0.01100079,0.01405185],"genre_scores_gemma":[0.8889406,0.0003799338,0.1057846,0.0002206052,0.00007963904,0.000126132,0.0003157641,0.00009605369,0.004056625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00729507,"threshold_uncertainty_score":0.01450521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005721201997318561,"score_gpt":0.2064510261276169,"score_spread":0.2007298241302984,"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."}}