{"id":"W1487896116","doi":"10.1007/978-3-540-24606-0_2","title":"Matrix Pencil for Positioning in Wireless ad hoc Sensor Network","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Matrix pencil; Multipath propagation; Computer science; Pencil (optics); Wireless ad hoc network; Wireless sensor network; Triangulation; Wireless; Algorithm; Matrix (chemical analysis); Real-time computing; Engineering; Computer network; Mathematics; 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":[0.0003013369,0.001698848,0.0007828328,0.0006070553,0.0003082678,0.001128153,0.00110212,0.0009206649,0.02363905],"category_scores_gemma":[0.001635107,0.000271781,0.0003460106,0.001685245,0.0005555691,0.001202636,0.0005557095,0.0010641,0.01275788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869387,"about_ca_system_score_gemma":0.0002617589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006727127,"about_ca_topic_score_gemma":0.0009548768,"domain_scores_codex":[0.9995022,0.0001626566,0.0000312085,0.00008179525,0.000201151,0.00002101419],"domain_scores_gemma":[0.999589,0.0001613595,0.00002650481,0.00009048136,0.0001149864,0.00001770054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001446951,0.00004175692,0.0001530836,0.0009011885,0.00003913089,0.0003692769,0.0001533384,0.04047985,0.01439361,0.1654129,0.1062546,0.6716567],"study_design_scores_gemma":[0.00004049997,0.0003549146,0.0003325052,0.0002612252,0.00004463247,0.001535222,0.0001409758,0.2697992,0.01636723,0.08971189,0.6213322,0.00007955079],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001182808,0.005894106,0.9678068,0.0004435493,0.003036917,0.00006053654,0.0003134124,0.001756088,0.01950573],"genre_scores_gemma":[0.07957546,0.01553178,0.7283224,0.0009783409,0.002805058,0.0005254961,0.001328957,0.00102502,0.1699075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02363905,"threshold_uncertainty_score":0.07908052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009817287662731804,"score_gpt":0.2232048579010485,"score_spread":0.2133875702383167,"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."}}