{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006264415,0.0005628856,0.0004423106,0.001088139,0.002632226,0.005004005,0.002057299,0.002386365,0.008063478],"category_scores_gemma":[0.01070173,0.0006189022,0.0008586109,0.0006852291,0.005540667,0.007576646,0.006206969,0.002016859,0.00113072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002010654,"about_ca_system_score_gemma":0.002638973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002734461,"about_ca_topic_score_gemma":0.003793181,"domain_scores_codex":[0.9978706,0.001245947,0.00004608562,0.0003659335,0.0002436672,0.0002278782],"domain_scores_gemma":[0.9967578,0.001427441,0.0002181777,0.0005843196,0.0003558632,0.0006563616],"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":[0.000250256,0.0003798158,0.01239111,0.0003414124,0.0001798359,0.0006143716,0.01322055,0.1355145,0.0083934,0.6868514,0.008846321,0.1330171],"study_design_scores_gemma":[0.00005065996,0.0002880029,0.0025816,0.000169158,0.0000388129,0.0003012258,0.007066687,0.2383896,0.002232126,0.6485765,0.1001971,0.0001086156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2340863,0.0007669574,0.6689213,0.01814895,0.0002184476,0.0003846963,0.0002179752,0.00101806,0.07623733],"genre_scores_gemma":[0.7732598,0.000304406,0.2116395,0.0006247519,0.00003187318,0.0002888667,0.0001822105,0.0002342216,0.01343434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008063478,"threshold_uncertainty_score":0.03312981,"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."}}