{"id":"W2187262079","doi":"","title":"Crowdsourcing with a Crowd of One and Other TREC 2011 Crowdsourcing and Web Track Experiments.","year":2011,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crowdsourcing; Computer science; Relevance (law); Set (abstract data type); Task (project management); Construct (python library); Quality (philosophy); Information retrieval; Data science; World Wide Web; Engineering","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":[0.02302221,0.001993962,0.001558824,0.002823271,0.005602545,0.003235876,0.001845807,0.002946877,0.005413402],"category_scores_gemma":[0.03996747,0.0009096643,0.001741172,0.002919089,0.001694628,0.002582507,0.004455783,0.003372033,0.003542606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004545857,"about_ca_system_score_gemma":0.005361124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04836946,"about_ca_topic_score_gemma":0.08975107,"domain_scores_codex":[0.9831612,0.007007731,0.0009466321,0.003174706,0.004358131,0.001351603],"domain_scores_gemma":[0.9632718,0.01174237,0.001086872,0.007885347,0.0106804,0.005333254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.008925825,0.01009774,0.03499249,0.002402719,0.00252483,0.001729197,0.006662318,0.1061157,0.04879063,0.01096115,0.5179401,0.2488572],"study_design_scores_gemma":[0.004949422,0.007711074,0.09802705,0.0003862738,0.0007607252,0.001066111,0.004300621,0.3589912,0.06988815,0.02941069,0.4230669,0.001441819],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6331785,0.001837096,0.1380646,0.005537674,0.008819821,0.01482901,0.06496193,0.0299811,0.1027902],"genre_scores_gemma":[0.6803602,0.00031915,0.1802871,0.001990117,0.00102777,0.007501869,0.071417,0.002536156,0.05456064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04836946,"threshold_uncertainty_score":0.1217546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03977264322084437,"score_gpt":0.2218622594840954,"score_spread":0.182089616263251,"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."}}