{"id":"W2898332948","doi":"10.1145/3265863.3268073","title":"Exploiting Mobility to Improve Underwater Sensor Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Underwater; Computer science; Computer network; Wireless sensor network; Key distribution in wireless sensor networks; Dual (grammatical number); Wireless network; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001572248,0.0001041583,0.0001093997,0.00003052836,0.00007526718,0.00006558725,0.0001812737,0.00005393541,0.0001501799],"category_scores_gemma":[0.000001206679,0.00008794916,0.000034236,0.0001043177,0.00002277372,0.00008208762,0.00008282872,0.00008015185,0.0004135306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000455889,"about_ca_system_score_gemma":0.000002930149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003875865,"about_ca_topic_score_gemma":0.00004251972,"domain_scores_codex":[0.9993018,0.00002416701,0.0002121192,0.0001432787,0.00007448642,0.000244164],"domain_scores_gemma":[0.9993634,0.00002417911,0.00001163488,0.0004476675,0.00005755615,0.00009552609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005862401,0.0001747967,0.005869812,0.0002030555,0.0003155177,0.0000051712,0.005714943,0.05594277,0.805191,0.00179965,0.005455375,0.1192693],"study_design_scores_gemma":[0.0004960796,0.0001761703,0.001544509,0.00005337096,0.00001351804,0.00001102461,0.001486154,0.440171,0.3729728,0.0003807654,0.1819497,0.000744821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4967769,0.00002125981,0.4906327,0.0001198675,0.0001491724,0.0001854747,0.000001188502,0.0005272821,0.01158615],"genre_scores_gemma":[0.9917861,0.000003793103,0.006773553,0.0002621624,0.0002945039,0.00003518152,0.00000190773,0.00002613449,0.0008166384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4950092,"threshold_uncertainty_score":0.5315238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850077564817379,"score_gpt":0.2292628766508708,"score_spread":0.210762101002697,"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."}}