{"id":"W4327568933","doi":"10.1038/s41893-023-01087-8","title":"The role of non-English-language science in informing national biodiversity assessments","year":2023,"lang":"en","type":"article","venue":"Nature Sustainability","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"English language; Local language; Language assessment; Computer science; Psychology; Mathematics education","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":[0.001536568,0.00006805945,0.000070127,0.00006729482,0.0003113828,0.00003535646,0.0004142019,0.00008587979,0.000790529],"category_scores_gemma":[0.00151714,0.00005239632,0.00003625758,0.001724288,0.0006445792,0.0003255534,0.000469048,0.0002552686,0.00004617574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00227511,"about_ca_system_score_gemma":0.0001287828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002629886,"about_ca_topic_score_gemma":0.000273829,"domain_scores_codex":[0.9986413,0.00002355143,0.0001351016,0.0002009341,0.000697817,0.0003013711],"domain_scores_gemma":[0.9994423,0.00009241056,0.0000589846,0.0001861912,0.0001703411,0.00004977554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003179073,0.00007951493,0.9834414,0.00001952148,0.000002323685,0.000003811087,0.003256495,0.00007262742,0.001318645,0.00191182,0.001686771,0.008175332],"study_design_scores_gemma":[0.0001492037,0.00001391823,0.9134856,0.000001459974,0.000001196805,2.218091e-7,0.07388114,0.0003453173,0.001584314,0.001143149,0.009332996,0.00006152342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672336,0.00001619138,9.3978e-7,0.0002738966,0.0001191417,0.0002000606,0.00004074731,0.00002842873,0.03208698],"genre_scores_gemma":[0.9998079,0.000006764446,0.000007184438,0.00004380851,0.000007742052,0.000008027566,0.00001914973,0.000001339465,0.00009808695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07062465,"threshold_uncertainty_score":0.8655738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005602808324323885,"score_gpt":0.2937511673506805,"score_spread":0.2881483590263566,"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."}}