{"id":"W2284343953","doi":"10.1111/cobi.12706","title":"Emerging problems of data quality in citizen science","year":2016,"lang":"en","type":"editorial","venue":"Conservation Biology","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":209,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Citizen science; Crowdsourcing; Citizen journalism; Open science; Data science; Open data; Resource (disambiguation); World Wide Web; Computer science; Political science; Public relations","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03392427,0.0001760493,0.0006194494,0.0007978627,0.0001000708,0.000109015,0.005622295,0.000361444,0.0002357338],"category_scores_gemma":[0.05627915,0.0001231673,0.00004353419,0.001326021,0.001243302,0.0009043169,0.002795293,0.0002434902,0.0001131935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007504869,"about_ca_system_score_gemma":0.0009809454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001291874,"about_ca_topic_score_gemma":0.001773841,"domain_scores_codex":[0.9940971,0.0008863985,0.001834588,0.001336117,0.001493566,0.0003522169],"domain_scores_gemma":[0.986258,0.007824603,0.0015048,0.003126443,0.001218798,0.00006740783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002246344,0.00002826774,0.00234826,0.00004019408,0.000008590725,4.710645e-7,0.00008338658,5.52996e-7,0.0005938141,0.01406106,0.9585156,0.02429733],"study_design_scores_gemma":[0.0003588688,0.00003070747,0.001854961,0.00008372658,0.000005126183,7.946777e-8,0.0000933007,0.00006785487,0.00002538256,0.04730366,0.9500223,0.0001540101],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00236915,0.0002668735,0.01545051,0.00897131,0.9609718,0.0007094137,0.006072109,0.00005096409,0.005137814],"genre_scores_gemma":[0.03865765,0.00238271,0.009695689,0.003657117,0.9068984,0.0002640222,0.02103679,0.0001072511,0.01730039],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.05407348,"threshold_uncertainty_score":0.9997578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3374571762887902,"score_gpt":0.5204741367644338,"score_spread":0.1830169604756435,"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."}}