{"id":"W1589636315","doi":"10.1155/2015/280674","title":"Localizing Wireless Sensors with Diverse Granularities in Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Granularity; Key distribution in wireless sensor networks; Node (physics); Software deployment; Sensor node; Wireless; Computer network; Distributed computing; Real-time computing; Wireless network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003295286,0.0003183192,0.000464911,0.000356791,0.00005217627,0.0001501793,0.0005300256,0.0002925438,0.00001659892],"category_scores_gemma":[0.00007100021,0.0002816911,0.00012934,0.0004796176,0.0001712656,0.0003423221,0.00007149029,0.000785533,0.000004408286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004255351,"about_ca_system_score_gemma":0.00005146375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004062599,"about_ca_topic_score_gemma":0.0000514626,"domain_scores_codex":[0.9978284,0.00008220392,0.0007653497,0.0001894954,0.0006796166,0.0004549332],"domain_scores_gemma":[0.9983656,0.0001333475,0.0002675137,0.0001858007,0.000865688,0.0001820391],"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":[0.0002596786,0.00004913632,0.01356032,0.00001000992,0.0002184533,0.0009977785,0.0001671173,0.9803274,0.00004029787,0.0004755776,0.001783298,0.002110936],"study_design_scores_gemma":[0.003041378,0.0001291861,0.002103013,0.0004171448,0.0000606909,0.0006964019,0.004279311,0.9860645,0.00101892,0.000132254,0.001548599,0.00050862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5448674,0.0003383662,0.4523902,0.0001935666,0.001615493,0.0001187595,0.00006105719,0.0002104051,0.0002047404],"genre_scores_gemma":[0.9981813,0.000355894,0.0006604875,0.00007089478,0.0005419971,0.000003708033,0.0001148283,0.00005156626,0.00001929717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4533139,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296386451929276,"score_gpt":0.2221009929482773,"score_spread":0.2091371284289845,"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."}}