{"id":"W2124486171","doi":"10.1109/wcnc.2009.4917915","title":"Power-aware Recovery for Geographic Routing","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Geographic routing; Computer science; Computer network; Probabilistic logic; Network packet; Routing protocol; Node (physics); Distributed computing; Energy consumption; Wireless ad hoc network; Dynamic Source Routing; Routing (electronic design automation); Wireless Routing Protocol; Wireless; Telecommunications; 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":[0.0002182535,0.0001294367,0.0001342745,0.0001146504,0.0001505531,0.0001583427,0.0006555078,0.00007726071,0.00001209712],"category_scores_gemma":[0.00002674862,0.0001173589,0.0001302307,0.0004022437,0.00001472997,0.000279827,0.00006703632,0.00008665644,0.00001284575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002009402,"about_ca_system_score_gemma":0.00001939585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006680091,"about_ca_topic_score_gemma":0.000004543463,"domain_scores_codex":[0.9987986,0.00002359624,0.0001961518,0.0003933676,0.0001746135,0.0004136421],"domain_scores_gemma":[0.9991293,0.0001450669,0.00006721502,0.0004990214,0.00007977246,0.00007963018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002136389,0.0002156159,0.0008406304,0.000006851082,0.00003020329,0.00001828835,0.0001998555,0.1309802,0.0006570145,0.6456162,0.006577858,0.2148359],"study_design_scores_gemma":[0.0005972588,0.00060307,0.004754702,0.00005140671,0.00000570243,0.00001943394,0.00003289795,0.977413,0.001951142,0.006793365,0.007218248,0.0005597773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01844877,0.00006646517,0.967646,0.001236056,0.0004633215,0.0001347757,6.832659e-7,0.0004715419,0.01153238],"genre_scores_gemma":[0.9330635,0.000009390165,0.0646152,0.001545837,0.00007171836,0.000005161376,0.000002594528,0.000007364363,0.0006791921],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9146148,"threshold_uncertainty_score":0.4785758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008352558085591748,"score_gpt":0.2222675283046203,"score_spread":0.2139149702190286,"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."}}