{"id":"W3202927809","doi":"10.1038/s41597-021-01006-6","title":"AusTraits, a curated plant trait database for the Australian flora","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"Australian Research Data Commons; NSW Department of Planning,Industry and Environment; Australian Research Council; Soochow University; Department of Biodiversity, Conservation and Attractions; Centre for Australian National Biodiversity Research; Department of Education and Training; Department of Environment, Land, Water and Planning, State Government of Victoria","keywords":"Taxon; Trait; Scope (computer science); Biology; Database; Ecology; Flora (microbiology); Field (mathematics); Taxonomic rank; Geography; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296227,0.0009316155,0.001051143,0.008406194,0.0009396868,0.001788922,0.001342395,0.0005297326,0.0185382],"category_scores_gemma":[0.005349631,0.0008363992,0.0007307941,0.01063257,0.000425214,0.002271973,0.002345104,0.001028039,0.008653001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266162,"about_ca_system_score_gemma":0.003058763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02300363,"about_ca_topic_score_gemma":0.03699773,"domain_scores_codex":[0.9990215,0.0001123288,0.0002006053,0.000298927,0.0002934193,0.00007324493],"domain_scores_gemma":[0.9974893,0.000479675,0.0005753356,0.0006045536,0.0005789484,0.0002722053],"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":[0.001372164,0.0002392228,0.05052667,0.01516901,0.0008359553,0.001448578,0.009950758,0.003414187,0.06582838,0.01247932,0.4249998,0.413736],"study_design_scores_gemma":[0.00007066874,0.00007639736,0.2143278,0.0004846079,0.0001803834,0.0006235968,0.000378461,0.002071437,0.002996015,0.004394832,0.7742777,0.0001181867],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0517695,0.001912801,0.0263856,0.0002999642,0.0001360465,0.0002975548,0.8881979,0.01510367,0.01589688],"genre_scores_gemma":[0.05750557,0.00105661,0.05194634,0.0001723258,0.0000450122,0.000619907,0.8785414,0.002728207,0.007384708],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02300363,"threshold_uncertainty_score":0.06201649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1633179462311934,"score_gpt":0.3183892448547478,"score_spread":0.1550712986235543,"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."}}