{"id":"W3210306003","doi":"10.1093/biosci/biab099","title":"Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity","year":2021,"lang":"en","type":"article","venue":"BioScience","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Sherbrooke; University of Toronto; Concordia University; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Leibniz-Gemeinschaft; Directorate for Biological Sciences; Leibniz-Institut für Gewässerökologie und Binnenfischerei; University of British Columbia; Centre National de la Recherche Scientifique; Université de Sherbrooke; University of Toronto; Concordia University; Deutsche Forschungsgemeinschaft; University of Aberdeen; McGill University; Universität Potsdam; KU Leuven; Freie Universität Berlin; University of Connecticut; National Science Foundation","keywords":"Biodiversity; Computer science; Environmental resource management; Biodiversity conservation; Risk analysis (engineering); Ecology; Business; Environmental science; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003785038,0.00007211065,0.00007369646,0.000008341513,0.0008121572,0.00009931171,0.0001022946,0.00003569009,0.0003233734],"category_scores_gemma":[0.0008042539,0.00006990284,0.00003356346,0.0002885128,0.0001301664,0.0002072051,0.0001672721,0.00003473454,0.00001355218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002682102,"about_ca_system_score_gemma":0.00002868902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000485124,"about_ca_topic_score_gemma":0.00007432397,"domain_scores_codex":[0.9991858,0.000009221249,0.00008866974,0.0003151252,0.0001264012,0.0002748015],"domain_scores_gemma":[0.9996905,0.00007379455,0.00005727072,0.00006968494,0.00002145269,0.00008734096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001482531,0.0001898775,0.3988841,0.0002683848,0.00001826419,0.00001014439,0.002228779,0.00002325165,0.5571905,0.003410283,0.008296156,0.02933195],"study_design_scores_gemma":[0.005728691,0.0009686332,0.3331912,0.000181624,0.0001184187,0.0002063123,0.08938314,0.01455655,0.4767008,0.001439323,0.07540198,0.002123326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703668,0.0000349678,0.02646658,0.0005109266,0.0001676113,0.0006525366,0.0002210993,0.00005791396,0.001521609],"genre_scores_gemma":[0.9926503,0.000007310282,0.006890203,0.0003306932,0.00001728309,0.00005201936,0.00001195953,0.000001836835,0.00003835396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08715437,"threshold_uncertainty_score":0.6246539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0869891462546319,"score_gpt":0.2797812723927061,"score_spread":0.1927921261380742,"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."}}