{"id":"W2744629383","doi":"10.18293/seke2017-102","title":"Multi-Objective Crowd Worker Selection in Crowdsourced Testing","year":2017,"lang":"en","type":"article","venue":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; Baidu","keywords":"Crowdsourcing; Selection (genetic algorithm); Computer science; Crowd sourcing; Data science; Machine learning; World Wide Web","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":[0.008962303,0.003030102,0.004398611,0.001859681,0.001771471,0.002015056,0.004456009,0.003099134,0.004379797],"category_scores_gemma":[0.02020359,0.001324171,0.001620058,0.001480118,0.002493457,0.002047493,0.004989367,0.001809675,0.001141601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001840834,"about_ca_system_score_gemma":0.002353686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009803594,"about_ca_topic_score_gemma":0.007109157,"domain_scores_codex":[0.9939427,0.003176773,0.0002170535,0.001350826,0.0008121708,0.0005003889],"domain_scores_gemma":[0.980248,0.0147344,0.001150332,0.001065982,0.001567759,0.001233549],"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":[0.001100473,0.0005702734,0.006061482,0.0008160633,0.0003052026,0.0009694319,0.001005592,0.8100144,0.005107746,0.01308715,0.007972402,0.1529899],"study_design_scores_gemma":[0.0001523443,0.0001880541,0.001038664,0.00007441603,0.00005475745,0.00008433431,0.0002647116,0.9774448,0.0009595612,0.01746676,0.002216573,0.0000550238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0567174,0.001284689,0.9334984,0.000946678,0.0002806873,0.0008154946,0.0003506549,0.001029224,0.005076755],"genre_scores_gemma":[0.7788483,0.0004603714,0.2096127,0.0008455627,0.0003053166,0.001447198,0.0009164947,0.000394845,0.007169326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009803594,"threshold_uncertainty_score":0.04739773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807087477580099,"score_gpt":0.2491263343932967,"score_spread":0.2210554596174957,"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."}}