{"id":"W2793871201","doi":"10.1609/hcomp.v5i1.13309","title":"Lessons from an Online Massive Genomics Computer Game","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Human Computation and Crowdsourcing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Genome Canada","keywords":"Computer science; Crowdsourcing; Casual; Task (project management); Data science; Matching (statistics); Citizen science; Artificial intelligence; Human–computer interaction; World Wide Web; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002451192,0.0005684962,0.0003319514,0.0009318375,0.0026519,0.004913573,0.001847546,0.002557483,0.01208772],"category_scores_gemma":[0.02004395,0.0002443642,0.0003631742,0.0006924024,0.002758628,0.00496656,0.002774019,0.002174542,0.002836496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599614,"about_ca_system_score_gemma":0.001593355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278135,"about_ca_topic_score_gemma":0.01600695,"domain_scores_codex":[0.9983087,0.0009742006,0.00004825368,0.0001864847,0.0002989864,0.0001835109],"domain_scores_gemma":[0.9924179,0.004776909,0.0002866594,0.0004127013,0.0008379792,0.001267909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0008859328,0.002149946,0.04068881,0.0008210607,0.0001230181,0.003028673,0.02656085,0.01379778,0.002297646,0.3535061,0.2483878,0.3077525],"study_design_scores_gemma":[0.0002037735,0.0006823791,0.01653207,0.0005617151,0.00004672795,0.001523024,0.02394872,0.03226104,0.001922619,0.4822639,0.4398724,0.000181636],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3504258,0.003742829,0.05539115,0.201293,0.002160606,0.0005696181,0.002031703,0.001035202,0.3833502],"genre_scores_gemma":[0.9399644,0.002031676,0.0167256,0.009025782,0.0004466167,0.0002634973,0.000676852,0.0003702278,0.03049535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01278135,"threshold_uncertainty_score":0.04043752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09804089691815192,"score_gpt":0.3325677333469798,"score_spread":0.2345268364288279,"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."}}