{"id":"W4286507075","doi":"10.1109/cits55221.2022.9832980","title":"Energy Harvesting WSNs with Adaptive Modulation: Inter-delivery-aware Scheduling Algorithms","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Fading; Wireless sensor network; Link adaptation; Scheduling (production processes); Randomness; Algorithm; Real-time computing; Energy harvesting; Energy (signal processing); Distributed computing; Mathematical optimization; Computer network; Decoding methods; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290268,0.0005668366,0.0007486666,0.0003444964,0.0004109543,0.0006135929,0.001131479,0.0006302681,0.0007837812],"category_scores_gemma":[0.003586377,0.0003199086,0.0003503781,0.0006265136,0.0004647652,0.000813448,0.000644436,0.0006869628,0.0001851802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007917518,"about_ca_system_score_gemma":0.001124401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591182,"about_ca_topic_score_gemma":0.001668738,"domain_scores_codex":[0.9994146,0.0001941325,0.0000347924,0.0001313447,0.0001449039,0.00008031255],"domain_scores_gemma":[0.9986953,0.0007426377,0.0002608279,0.000121985,0.0001279635,0.00005126644],"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":[0.00007808282,0.00006288652,0.0005122748,0.00005812847,0.00002555455,0.00003434512,0.00005489162,0.9312001,0.003519791,0.01021382,0.0006798247,0.05356023],"study_design_scores_gemma":[0.00000820103,0.00002151774,0.00006524413,0.000002608464,0.000003278975,0.00001224911,0.000005840134,0.9967681,0.0003832258,0.002470142,0.0002572782,0.000002372656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01872001,0.0003934332,0.9792395,0.000174972,0.00004955104,0.00004513313,0.00002235699,0.0001361163,0.001218903],"genre_scores_gemma":[0.7871615,0.0004162146,0.2107423,0.0001155212,0.00007502297,0.0001300496,0.00005548659,0.00004298714,0.001260846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001591182,"threshold_uncertainty_score":0.006823659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455821860297806,"score_gpt":0.1919789983142177,"score_spread":0.1774207797112397,"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."}}