{"id":"W2809480882","doi":"10.1111/gcb.14358","title":"Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research","year":2018,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Armed Forces; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Species richness; Citizen science; Vetting; Global biodiversity; Biodiversity; Data collection; Diversity (politics); Data quality; Ecology; Geography; Environmental resource management; Business; Computer science; Biology; Environmental science; Statistics; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009603875,0.000147352,0.0002135264,0.00004244643,0.001114692,0.00002983425,0.0009379892,0.0001301637,0.001765246],"category_scores_gemma":[0.0001916156,0.0001362463,0.00003398599,0.0007884842,0.006926431,0.0003055571,0.002463705,0.00006907325,0.0000537456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908404,"about_ca_system_score_gemma":0.00002767409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451996,"about_ca_topic_score_gemma":0.001764782,"domain_scores_codex":[0.998085,0.00006330632,0.0001872045,0.0006209135,0.0002927456,0.000750883],"domain_scores_gemma":[0.9991576,0.00005005741,0.00008039695,0.0004188541,0.0000825813,0.0002104953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001769027,0.0001488875,0.6144142,0.00003677312,0.00002594555,0.000004144323,0.0004923693,1.398909e-9,0.0004429727,0.3707634,0.006492998,0.007001445],"study_design_scores_gemma":[0.001437659,0.001301233,0.8853051,0.00002931652,0.0000663555,0.0000468996,0.006445339,0.0001385965,0.0001071499,0.04799803,0.05669473,0.000429617],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678176,0.000498009,0.006344416,0.005142763,0.001490271,0.002183363,0.07483836,0.000114313,0.04157089],"genre_scores_gemma":[0.9982384,0.0003148726,0.00007862769,0.0001357734,0.000142333,0.00003146476,0.001033114,0.000003182241,0.00002226555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3227654,"threshold_uncertainty_score":0.9991473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5482681189265849,"score_gpt":0.4184027521060706,"score_spread":0.1298653668205142,"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."}}