{"id":"W3159852056","doi":"10.1145/3451161","title":"Assessing Top- Preferences","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Crowdsourcing; Preference; Learning to rank; Artificial intelligence; Information retrieval; Rank (graph theory); Measure (data warehouse); Quality (philosophy); Machine learning; Natural language processing; Data mining; Ranking (information retrieval); Statistics; Mathematics; 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":[0.009768994,0.001237262,0.001103632,0.005027038,0.001248165,0.004345824,0.0009161801,0.001382152,0.01186385],"category_scores_gemma":[0.06458999,0.0003317514,0.000921298,0.004005476,0.0007695613,0.00469779,0.002393749,0.001347031,0.005865947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008841041,"about_ca_system_score_gemma":0.001551808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004294001,"about_ca_topic_score_gemma":0.006621578,"domain_scores_codex":[0.9863732,0.003804055,0.001227398,0.002173737,0.00574084,0.0006807558],"domain_scores_gemma":[0.9618919,0.01543158,0.003195126,0.005336393,0.01225674,0.001888187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002134162,0.0004331932,0.2312041,0.001049421,0.000790916,0.0002871069,0.001688997,0.01481602,0.016108,0.01620462,0.02619396,0.6890895],"study_design_scores_gemma":[0.0004001119,0.003083273,0.4221509,0.0005196202,0.0009273153,0.002281611,0.006488335,0.2764718,0.05180804,0.111865,0.1232194,0.0007845481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4566419,0.003004207,0.4287954,0.001478293,0.0004165236,0.0009287878,0.01181912,0.004243605,0.09267222],"genre_scores_gemma":[0.9147259,0.0003397591,0.07329802,0.0002490909,0.0001423294,0.0002294374,0.00324014,0.0003536384,0.007421747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01186385,"threshold_uncertainty_score":0.05166399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042962377851257,"score_gpt":0.2664194563674335,"score_spread":0.2359898325889209,"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."}}