{"id":"W4311524705","doi":"10.22323/1.418.0122","title":"Exploring CrowdBots: a new evolutionary pathway for citizen science projects","year":2022,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Crowdsourcing; Artificial intelligence; Leverage (statistics); Computer science; Machine learning; Inference; Citizen science; Reinforcement learning; Data science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00080806,0.0001362296,0.0001338265,0.0002819284,0.001621347,0.0002725182,0.001191514,0.00001383206,0.0000318753],"category_scores_gemma":[0.000130767,0.0001364145,0.00008290524,0.001313332,0.00009043906,0.001044311,0.0009485865,0.0001473632,0.0000112937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000248853,"about_ca_system_score_gemma":0.001111828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009715837,"about_ca_topic_score_gemma":0.000002626908,"domain_scores_codex":[0.9979286,0.00003886164,0.0002082735,0.0006641672,0.0006027035,0.0005573594],"domain_scores_gemma":[0.9989023,0.0001149108,0.00006862881,0.0006279363,0.0001094785,0.0001767882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006134802,0.0002666918,0.0004972506,0.00007119501,0.00003252535,0.00006852483,0.01343697,0.01145459,0.07810389,0.5247757,0.03742273,0.3338085],"study_design_scores_gemma":[0.003133102,0.00151158,0.003265547,0.0001115788,0.00002085393,0.0007281768,0.00623437,0.5998388,0.09482242,0.02917302,0.2590717,0.002088824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1435936,0.0002003081,0.8405954,0.001962954,0.001970712,0.0008337917,0.000006014389,0.001003857,0.009833271],"genre_scores_gemma":[0.889045,0.000002343389,0.1073079,0.0004736283,0.0001693918,0.0003105789,0.000001744457,0.00001496579,0.002674465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7454513,"threshold_uncertainty_score":0.9996784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1216173751094976,"score_gpt":0.2531382161979859,"score_spread":0.1315208410884883,"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."}}