{"id":"W2164824354","doi":"10.1109/infcom.2004.1354686","title":"Rigidity, computation, and randomization in network localization","year":2005,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":521,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Rigidity (electromagnetism); Construct (python library); Graph theory; Computer science; Computation; Theoretical computer science; Belief propagation; Algorithm; Mathematics; Discrete mathematics; Combinatorics; Computer network; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009597012,0.00006719545,0.00009074292,0.00008133839,0.0000342183,0.00002464151,0.0000319757,0.00006777072,0.00003503358],"category_scores_gemma":[0.00001940152,0.00006543445,0.000008860054,0.0002636636,0.00002062566,0.0001397178,0.00001186676,0.00004521313,0.000011577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003123736,"about_ca_system_score_gemma":0.000003266726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000573385,"about_ca_topic_score_gemma":0.0001221364,"domain_scores_codex":[0.9995788,0.00001232264,0.0001674275,0.00007527121,0.00005503739,0.0001110908],"domain_scores_gemma":[0.9998763,0.00002726784,0.00001194691,0.00004642735,0.00002267088,0.0000153847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004312483,0.000003631799,0.004456575,0.000008713375,0.000002445273,2.273881e-7,0.00005631668,0.9753653,0.000007345435,0.004026059,0.003193059,0.01287602],"study_design_scores_gemma":[0.001071939,0.000004143687,0.001238234,0.00000920769,0.000002431361,0.000001510739,0.00002471859,0.9913234,0.000715561,0.002200726,0.003320823,0.00008729125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01390404,0.0002451064,0.9795296,0.0001085209,0.00008244985,0.0001301571,4.148945e-7,0.0005853042,0.005414435],"genre_scores_gemma":[0.9948399,0.0001871986,0.004737774,0.00009990274,0.00006046914,0.000007432585,0.00001713518,0.00001086515,0.00003931286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809359,"threshold_uncertainty_score":0.2668339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004680104294377368,"score_gpt":0.1974195554208336,"score_spread":0.1927394511264563,"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."}}