{"id":"W4414603255","doi":"10.1109/jiot.2025.3615410","title":"LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-Assisted Sensor Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scheduling (production processes); Reinforcement learning; Wireless sensor network; Data collection; Schedule; Network packet; Drone; Task analysis","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":[0.0005767236,0.0006046143,0.0005232243,0.0002461533,0.0003673597,0.0004443717,0.00107382,0.0004195739,0.0007397837],"category_scores_gemma":[0.00214181,0.0002414489,0.0002918082,0.0002997558,0.000414248,0.001037566,0.001053981,0.001092434,0.0001665416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017424,"about_ca_system_score_gemma":0.001494865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007992334,"about_ca_topic_score_gemma":0.01430742,"domain_scores_codex":[0.9995756,0.0001060493,0.00002668692,0.0001253146,0.00009081469,0.00007558276],"domain_scores_gemma":[0.9993693,0.0002550387,0.00009088466,0.0001050008,0.0001154738,0.00006429249],"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":[0.0003789197,0.0003376188,0.003090552,0.0001754616,0.0000516203,0.0002184853,0.0002980768,0.7279618,0.0213749,0.005311861,0.003740451,0.2370602],"study_design_scores_gemma":[0.000007611917,0.00003288955,0.0001728604,0.00000342091,0.000004343087,0.00001288797,0.00001871995,0.9959444,0.002092017,0.001188513,0.0005170107,0.000005238713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1091905,0.00125059,0.8820951,0.0006067336,0.0001991319,0.0001164129,0.0002161365,0.003964581,0.002360792],"genre_scores_gemma":[0.9400564,0.0001535719,0.05845632,0.0002051304,0.00003458788,0.00008078859,0.0001503174,0.00006196361,0.000800876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007992334,"threshold_uncertainty_score":0.01589161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471682922518389,"score_gpt":0.2761115936959591,"score_spread":0.2513947644707752,"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."}}