{"id":"W4393317822","doi":"10.1080/09505431.2024.2332287","title":"Introduction: digital participatory biodiversity science","year":2024,"lang":"en","type":"article","venue":"Science as Culture","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Interoperability; Citizen journalism; Context (archaeology); Citizen science; Agency (philosophy); Data science; Biodiversity; Engineering ethics; Sociology; Computer science; Political science; Engineering; Geography; World Wide Web; Social science; Ecology","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000449069,0.0001009041,0.00006072641,0.00005882324,0.0008293566,0.0008450425,0.000588398,0.00003164661,0.04403679],"category_scores_gemma":[0.0001383895,0.00007653896,0.00004077248,0.002704204,0.004642446,0.002455706,0.0003989482,0.0001196682,0.0283443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008132296,"about_ca_system_score_gemma":0.00009193498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002199745,"about_ca_topic_score_gemma":0.000007495573,"domain_scores_codex":[0.9979841,0.000005324638,0.00008785675,0.0006127802,0.0008743084,0.0004356469],"domain_scores_gemma":[0.9994562,0.000004429492,0.00001896151,0.000246368,0.00003479439,0.0002392536],"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":[0.00001207037,0.0001382202,0.01629958,0.00001592252,0.000006166521,0.00005324216,0.005499474,0.00003322545,0.2035861,0.02981997,0.7347111,0.009824883],"study_design_scores_gemma":[0.00006169987,0.00005887294,0.02375162,0.000005721082,0.000007471854,0.00004863016,0.004924871,0.00009609949,0.01958166,0.0003880815,0.9508612,0.0002140512],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.853343,0.00009344968,0.0000153795,0.004894269,0.001294145,0.0001001386,0.00004835224,0.0002114626,0.1399998],"genre_scores_gemma":[0.9964007,0.00001502746,0.00001914216,0.0003043801,0.0001231364,0.000004193619,0.000008921378,0.000002095644,0.003122411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2161501,"threshold_uncertainty_score":0.9980664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02509558540167135,"score_gpt":0.2713082457849189,"score_spread":0.2462126603832475,"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."}}