{"id":"W3109224004","doi":"10.17762/de.vi.974","title":"The research on a Multipath Forwarding Strategy in Wireless Sensor Networks","year":2020,"lang":"en","type":"article","venue":"Design Engineering","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer network; Computer science; Path (computing); Reliability (semiconductor); Multipath propagation; Wireless sensor network; Connection (principal bundle); Distributed computing; Quality of service; Topology (electrical circuits); Channel (broadcasting); Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001240192,0.0001935881,0.0001869635,0.0001432281,0.0002062343,0.0003022252,0.001026689,0.0001068341,7.633806e-7],"category_scores_gemma":[0.0001459756,0.0001587023,0.0000538552,0.001425052,0.00003226482,0.000151415,0.0001937689,0.0008143839,0.00001673336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009988988,"about_ca_system_score_gemma":0.00003397625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001388621,"about_ca_topic_score_gemma":0.000003626631,"domain_scores_codex":[0.9976947,0.0002519613,0.0002709532,0.0004817581,0.0004653759,0.0008352922],"domain_scores_gemma":[0.9975066,0.001762959,0.00003797472,0.0004544472,0.00006711201,0.0001708846],"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":[0.0000155428,0.0000123198,0.00002903277,0.000005853503,0.000007457342,0.00008073978,0.0001873661,0.9785801,0.001109783,0.01406305,0.0001559662,0.005752858],"study_design_scores_gemma":[0.0002318988,0.0001014588,0.0001996783,0.00006108644,8.977936e-7,0.000003540647,0.0000449863,0.9975683,0.001331214,0.000009293153,0.000271904,0.0001757541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01950846,0.0001426448,0.978902,0.0004855203,0.0002020855,0.0002582533,2.20996e-7,0.0002689185,0.0002318956],"genre_scores_gemma":[0.9867404,0.00005904579,0.01279131,0.00008387142,0.0002231075,0.00004246923,6.346883e-7,0.00003495629,0.000024211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9672319,"threshold_uncertainty_score":0.647169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08878321147705974,"score_gpt":0.2847985509997254,"score_spread":0.1960153395226656,"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."}}