{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002294383,0.0004837982,0.0004769169,0.001594006,0.0006162874,0.001664835,0.002417514,0.0002532226,0.00002074997],"category_scores_gemma":[0.0002197177,0.0004395671,0.0001246633,0.0009938681,0.0003241249,0.00234534,0.001674196,0.001000594,0.0001477285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004311791,"about_ca_system_score_gemma":0.0005267911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001585498,"about_ca_topic_score_gemma":0.0000608167,"domain_scores_codex":[0.996016,0.00005258049,0.0008855931,0.0007526093,0.00163388,0.0006593781],"domain_scores_gemma":[0.9971483,0.0003096054,0.0004149821,0.001304264,0.0005710205,0.0002518643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001479419,0.00004049159,0.0001802727,0.00007881864,0.00001224486,0.001079475,0.01862159,0.01157672,0.00002225805,0.005203297,0.00001740809,0.9631526],"study_design_scores_gemma":[0.0008479045,0.0007509859,0.0001764634,0.0002409469,0.00001749629,0.002588102,0.00001217619,0.9828101,0.0006140296,0.008189458,0.002666482,0.00108579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007744059,0.00002678634,0.9883485,0.0002155558,0.001167626,0.0007629954,0.000001644045,0.0002369605,0.001495848],"genre_scores_gemma":[0.911401,0.000003418317,0.08707627,0.0009869841,0.0002680171,0.00000687801,0.00000513578,0.00001987959,0.0002323878],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9712334,"threshold_uncertainty_score":0.9998056,"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."}}