{"id":"W7083697720","doi":"10.1016/j.cor.2025.107292","title":"Dynamic crowdsourcing problem in urban–rural distribution using the learning-based approach","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Heuristic; Markov decision process; Matching (statistics); Process (computing); Total revenue; Task (project management); Partially observable Markov decision process; Revenue","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002240219,0.00009845357,0.0001405464,0.0002541697,0.003873504,0.0005277145,0.0003754169,0.00007758666,0.00001321999],"category_scores_gemma":[0.0004001206,0.00007989717,0.0000519728,0.001944796,0.0004816722,0.0002009139,0.0001332219,0.0005396653,0.000007594812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005872682,"about_ca_system_score_gemma":0.0006714927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003743603,"about_ca_topic_score_gemma":0.002508229,"domain_scores_codex":[0.9971935,0.001324539,0.0002463,0.0002518406,0.0005256089,0.0004582753],"domain_scores_gemma":[0.9987532,0.0005359533,0.00002048776,0.0001873386,0.0004522737,0.00005067429],"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":[0.00001790039,0.00015794,0.02023615,0.00002733522,0.00003323938,0.000001760387,0.02000959,0.9245222,0.0001057258,0.02875588,0.003332082,0.002800213],"study_design_scores_gemma":[0.0002977384,0.00002086335,0.003611066,0.00006597674,0.000005700509,2.33378e-7,0.01116002,0.9762325,0.00001360418,0.000143602,0.008354676,0.0000940506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.14587,0.0006499499,0.8386925,0.007717377,0.0002367806,0.001256856,0.00000807266,0.0001059425,0.005462477],"genre_scores_gemma":[0.995544,0.00003052971,0.002023227,0.00007042747,0.00006730963,0.00008791153,0.00005621207,0.000006739484,0.002113618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.849674,"threshold_uncertainty_score":0.9974233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06765207153934012,"score_gpt":0.4068248571242601,"score_spread":0.33917278558492,"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."}}