{"id":"W2951194580","doi":"10.48550/arxiv.1809.05234","title":"In-Route Task Selection in Crowdsourcing","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ciência sem Fronteiras; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Task (project management); Crowdsourcing; Heuristic; Computer science; Set (abstract data type); Selection (genetic algorithm); Skyline; Path (computing); Order (exchange); Artificial intelligence; Data mining; World Wide Web; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005976124,0.0003538078,0.0004056709,0.0008081761,0.0001384057,0.0002118879,0.001224993,0.0004070815,0.00001919093],"category_scores_gemma":[0.00005439473,0.0004518905,0.0001484485,0.001422041,0.0001011029,0.0005249415,0.001359047,0.0009476452,0.00009632635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000642401,"about_ca_system_score_gemma":0.0002485324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001345529,"about_ca_topic_score_gemma":0.001000192,"domain_scores_codex":[0.9973462,0.0002351927,0.000319055,0.001412398,0.0001083431,0.0005788056],"domain_scores_gemma":[0.9986219,0.00009417255,0.0002328106,0.0008091409,0.0001142,0.0001277504],"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":[0.00007188626,0.0002689837,0.07764171,0.0002101672,0.00006606994,0.001604352,0.002924981,0.8811355,0.001182965,0.032067,0.001202066,0.001624262],"study_design_scores_gemma":[0.0007658501,0.00006041691,0.0139252,0.0004311035,0.00002173919,0.00002601961,0.00009601002,0.9588314,0.0007070457,0.02321238,0.00116873,0.0007540468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7545522,0.00002933526,0.2419631,0.0000844744,0.0006179955,0.0002230944,0.000001923504,0.0001897381,0.002338032],"genre_scores_gemma":[0.9970087,0.0000296054,0.001507327,0.0001339931,0.0001663364,0.000001312447,0.000003348616,0.00002389353,0.001125425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2424565,"threshold_uncertainty_score":0.9997933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223586289160964,"score_gpt":0.1846513579554552,"score_spread":0.1424154950638456,"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."}}