{"id":"W7124132508","doi":"10.65109/lvmh6964","title":"Improving the efficiency of crowdsourcing contests","year":2014,"lang":"","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crowdsourcing; Incentive; Mechanism design; Mechanism (biology); Set (abstract data type); Production (economics)","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":[0.01913041,0.001666076,0.003778072,0.001615055,0.002039582,0.00388632,0.004361053,0.003089409,0.0072005],"category_scores_gemma":[0.06295272,0.0008695038,0.00141168,0.001491204,0.002943575,0.005341199,0.005925443,0.002352644,0.001478612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001896855,"about_ca_system_score_gemma":0.002927798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824817,"about_ca_topic_score_gemma":0.001348836,"domain_scores_codex":[0.9878994,0.006621318,0.0005759225,0.001643394,0.001750778,0.001509344],"domain_scores_gemma":[0.9501741,0.03423234,0.004881477,0.005985147,0.002240523,0.002486493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001853824,0.001140283,0.007571398,0.0009293394,0.0003151159,0.0005668222,0.00156518,0.5099638,0.01529131,0.2938076,0.007107097,0.1598882],"study_design_scores_gemma":[0.0003139479,0.0004888744,0.00114991,0.00006901717,0.00005972967,0.0001640558,0.0003387351,0.8151569,0.003231941,0.1737878,0.005181449,0.00005770974],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2324458,0.0008000649,0.7409564,0.001954899,0.0001537841,0.0007501071,0.0002067644,0.0007822319,0.02194984],"genre_scores_gemma":[0.9322283,0.0002402168,0.06126527,0.0002181287,0.00006963102,0.0002736161,0.00009675093,0.0001372003,0.005470817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01913041,"threshold_uncertainty_score":0.1011725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009386567750588336,"score_gpt":0.209532142980758,"score_spread":0.2001455752301697,"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."}}