{"id":"W45142999","doi":"10.1007/978-3-319-06028-6_68","title":"Bringing Information Retrieval into Crowdsourcing: A Case Study","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Crowdsourcing; Computer science; Information retrieval; Quality (philosophy); Human–computer information retrieval; Work (physics); Control (management); Crowdsourcing software development; Data science; World Wide Web; Artificial intelligence; Search engine; 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":[0.007438146,0.0007904069,0.0006817442,0.002498236,0.00979582,0.004638294,0.00326673,0.0063313,0.006062942],"category_scores_gemma":[0.0235154,0.0004533507,0.0009614679,0.004625297,0.002743465,0.003226866,0.004190026,0.002267846,0.001830064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711749,"about_ca_system_score_gemma":0.00310889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01722803,"about_ca_topic_score_gemma":0.02855394,"domain_scores_codex":[0.9915782,0.00466375,0.0003348559,0.0006331062,0.001709252,0.001080814],"domain_scores_gemma":[0.9697893,0.02277821,0.001353462,0.002477777,0.001621344,0.001979897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003362274,0.0207948,0.05944372,0.003697163,0.0003558763,0.07743872,0.3578109,0.01335955,0.0157783,0.03851096,0.04671934,0.3627283],"study_design_scores_gemma":[0.001358158,0.005478994,0.0576093,0.001288431,0.0004265804,0.03012568,0.4968545,0.05188773,0.02162138,0.0370294,0.2957639,0.0005559443],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370192,0.000668597,0.01005439,0.00369014,0.000136173,0.001273472,0.0005354037,0.000209898,0.04641275],"genre_scores_gemma":[0.9683701,0.0005605294,0.01467706,0.0006836019,0.0001077334,0.00049972,0.0003112497,0.0001189027,0.01467105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01722803,"threshold_uncertainty_score":0.0393371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782925375894799,"score_gpt":0.2650220393064809,"score_spread":0.2471927855475329,"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."}}