{"id":"W4249706777","doi":"10.32920/ryerson.14653950","title":"REEP : a data-centric, reliable and energy-efficient routing protocol for wireless sensor networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Computer science; Routing protocol; Computer network; Wireless Routing Protocol; Key distribution in wireless sensor networks; Zone Routing Protocol; Mobile wireless sensor network; Reliability (semiconductor); Interior gateway protocol; Protocol (science); Distributed computing; Routing (electronic design automation); Wireless; 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","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001333041,0.0009232307,0.001094176,0.0002474989,0.0005129821,0.00190676,0.003980114,0.0008767294,0.00001939318],"category_scores_gemma":[0.0001097264,0.0008732547,0.0002406494,0.0007881774,0.0001305893,0.0003031484,0.01410714,0.001020742,0.000001956938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075119,"about_ca_system_score_gemma":0.0004574917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003600011,"about_ca_topic_score_gemma":0.0001125779,"domain_scores_codex":[0.9925489,0.000238159,0.001218884,0.003671252,0.0008326265,0.001490171],"domain_scores_gemma":[0.9923636,0.0005805376,0.0008183506,0.005224343,0.0005985518,0.0004146733],"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":[0.00003721841,0.0002926884,0.00008113185,0.0002403794,0.00009186004,0.00006529425,0.0001126038,0.9618235,0.0000185769,0.01620821,0.003772707,0.01725587],"study_design_scores_gemma":[0.001174258,0.00005719334,0.00002082898,0.0007349376,0.00004162797,0.00004233146,0.00005964594,0.9803014,0.0002768696,0.00005025489,0.01624447,0.0009961369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004402876,0.0001597974,0.9538808,0.0004354443,0.001271795,0.04147013,0.00001869203,0.0007619898,0.001561031],"genre_scores_gemma":[0.05620969,0.0001271539,0.6538158,0.001414938,0.002118713,0.2805887,0.0008599938,0.0003340206,0.004530955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.300065,"threshold_uncertainty_score":0.9993718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0372317495571902,"score_gpt":0.2844209696038495,"score_spread":0.2471892200466594,"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."}}