{"id":"W4401496685","doi":"10.1142/s230138502550058x","title":"AquaFly Project: Autonomous Multi-Drone Water Sampling with a Payload Deployment and Retraction Mechanism","year":2024,"lang":"en","type":"article","venue":"Unmanned Systems","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Payload (computing); Software deployment; Drone; Mechanism (biology); Computer science; Sampling (signal processing); Computer security; Telecommunications; Operating system; Physics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003282092,0.0002193858,0.0002360642,0.000165377,0.0001182432,0.0003948137,0.0001281569,0.0001302298,0.00000494849],"category_scores_gemma":[6.535475e-7,0.0001489471,0.00003617726,0.0001213748,0.00001694144,0.0002257986,0.00004069504,0.0002338898,0.00008864523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001568532,"about_ca_system_score_gemma":0.00001622537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003493588,"about_ca_topic_score_gemma":0.00007383937,"domain_scores_codex":[0.9988056,0.00006096769,0.0003575029,0.0002806955,0.0001894464,0.0003058304],"domain_scores_gemma":[0.9995029,0.00002939631,0.0000269283,0.0003352672,0.00003627631,0.000069209],"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":[0.0001156908,0.0002377804,0.0005028467,0.007358446,0.001766185,0.0001773397,0.03670601,0.02471166,0.8972977,0.006471391,0.0008817816,0.02377317],"study_design_scores_gemma":[0.001069565,0.0002723696,0.00009899528,0.001479174,0.0001081913,0.0006357956,0.0043102,0.7833043,0.05358596,0.0001161429,0.1541219,0.0008974255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4478487,0.006971646,0.5368482,0.0003018851,0.001471474,0.002087613,0.00003960071,0.003399739,0.001031107],"genre_scores_gemma":[0.9971521,0.0000933742,0.001390104,0.00001080059,0.0001360158,0.0002180254,0.00002690847,0.00008092402,0.0008916877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8437117,"threshold_uncertainty_score":0.6073886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03334147493509215,"score_gpt":0.246005521904991,"score_spread":0.2126640469698989,"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."}}