{"id":"W4402156327","doi":"10.1109/icc51166.2024.10622697","title":"Task Assignment in Extreme Edge Sensing: Balancing Response Time and Incentives","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Incentive; Task (project management); Enhanced Data Rates for GSM Evolution; Distributed computing; Telecommunications; Engineering; Microeconomics","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":[0.0001502525,0.0001027082,0.0001159763,0.00007469771,0.00003359308,0.00006388408,0.00004058166,0.00003080507,0.00005960757],"category_scores_gemma":[0.00002803936,0.00008534306,0.00002215073,0.0001488919,0.00008117146,0.0001021552,0.0001114518,0.0001404968,0.00006271793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004678354,"about_ca_system_score_gemma":0.00001720828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001576681,"about_ca_topic_score_gemma":0.000001579149,"domain_scores_codex":[0.999339,0.00003667781,0.0001215606,0.0002370185,0.00006967353,0.0001960197],"domain_scores_gemma":[0.9995675,0.0002555556,0.00001305904,0.0001237323,0.000009243058,0.00003087425],"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":[0.0002016378,0.00009951813,0.006684221,0.00002035398,0.00008011193,0.0001422607,0.0006387276,0.0002739502,0.5081636,0.06210459,0.00146969,0.4201214],"study_design_scores_gemma":[0.00207696,0.0004118921,0.02282541,0.001504831,0.00008643512,0.00002624141,0.004453047,0.3861073,0.2133762,0.2984826,0.06845578,0.00219325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8930552,0.0002137211,0.09752989,0.001575832,0.00009007235,0.0001507244,0.000006697287,0.0003662369,0.007011638],"genre_scores_gemma":[0.9878427,0.000001898249,0.01062601,0.00001620907,0.00003005834,9.797474e-7,0.000002327072,0.00001149012,0.001468346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4179281,"threshold_uncertainty_score":0.3480189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01772606297191908,"score_gpt":0.2445935976911658,"score_spread":0.2268675347192467,"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."}}