{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007703733,0.0001151758,0.00009302679,0.00001461067,0.0005841045,0.0004665077,0.001364071,0.00003814257,0.09820227],"category_scores_gemma":[0.0002049936,0.00008319599,0.00004086459,0.0005762659,0.0004504893,0.0004379178,0.0009690929,0.00009540295,0.001476564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009792143,"about_ca_system_score_gemma":0.00004378825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167964,"about_ca_topic_score_gemma":0.002244806,"domain_scores_codex":[0.998287,0.00003156424,0.0001838085,0.0007316733,0.000381373,0.0003845975],"domain_scores_gemma":[0.9979277,0.00007162326,0.00005412386,0.001795743,0.00002347758,0.0001273305],"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":[0.000007748596,0.00006885041,0.0001105423,0.000007999206,0.000009306857,0.00001111891,0.00006598049,0.000005521892,0.01333066,0.0006996522,0.9837472,0.00193537],"study_design_scores_gemma":[0.0002627981,0.000007047262,0.0069037,0.000007689567,0.00003000101,0.00002063388,0.001839855,0.0009898209,0.003583532,0.00004726565,0.9861785,0.0001292253],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09880063,0.0002966201,0.002545455,0.02424484,0.007641576,0.001560773,0.8506601,0.0002563779,0.01399366],"genre_scores_gemma":[0.3347498,0.0000952956,0.002370357,0.001573691,0.0003594341,0.0001681043,0.5424934,0.00005891623,0.118131],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3081667,"threshold_uncertainty_score":0.9993009,"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."}}