{"id":"W2744382302","doi":"","title":"Using Citizen Science for Water Quality Monitoring: Preaching the Message Beyond the Choir","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Choir; Citizen science; Quality (philosophy); Computer science; Political science; Sociology; Epistemology; Pedagogy; Philosophy; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.004606594,0.0001435653,0.0001214998,0.00001647062,0.001096434,0.0002427442,0.0007120701,0.00004398897,0.0001576621],"category_scores_gemma":[0.0005406775,0.00007267874,0.00006443992,0.0001933675,0.0005368558,0.0003022387,0.0005317743,0.00014682,0.0001373476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000642182,"about_ca_system_score_gemma":0.00002376407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002399435,"about_ca_topic_score_gemma":0.0003022925,"domain_scores_codex":[0.9981042,0.0001196477,0.0002429603,0.0003229433,0.0006530408,0.0005572163],"domain_scores_gemma":[0.9991484,0.0001333098,0.0001382201,0.0003862613,0.00005094141,0.0001428145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009036579,0.0001592131,0.4499312,0.00004016355,0.00003639606,0.000003439747,0.02756401,0.001978164,0.4983115,0.00295728,0.01568311,0.003245153],"study_design_scores_gemma":[0.003952339,0.0002523576,0.296912,0.0001729725,0.0002234774,0.00006800422,0.1493261,0.01566194,0.2957393,0.007662221,0.2277141,0.002315172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604967,0.00008018977,0.0002229438,0.001590005,0.0005055443,0.0003387164,0.00001485243,0.00004951152,0.03670149],"genre_scores_gemma":[0.9988096,0.000004228276,0.0003784451,0.0002036639,0.0001875662,0.00003685795,0.000006677029,0.00001377878,0.000359231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2120309,"threshold_uncertainty_score":0.8432992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1393159910573286,"score_gpt":0.3609962366678876,"score_spread":0.221680245610559,"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."}}