{"id":"W4200153775","doi":"10.3897/biss.5.79084","title":"Challenges in Curating Interdisciplinary Data in the Biodiversity Research Community","year":2021,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Museum and Library Services","keywords":"Biodiversity; Data science; Computer science; World Wide Web; Sociology; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.01384716,0.00009546251,0.0001131676,0.0001932038,0.002295737,0.000182589,0.001359122,0.00005005494,0.00009056272],"category_scores_gemma":[0.0008780703,0.00008025346,0.00001460463,0.001184512,0.002135075,0.004401127,0.009453988,0.000470719,0.0002083467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009260386,"about_ca_system_score_gemma":0.00006707207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007123602,"about_ca_topic_score_gemma":0.002140421,"domain_scores_codex":[0.9966401,0.0003622577,0.0001778157,0.0002600124,0.002228779,0.0003310003],"domain_scores_gemma":[0.9989499,0.0001818313,0.00005594784,0.0006267304,0.000123883,0.00006174813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000333066,0.0001177782,0.8796103,0.00002738952,0.000003573167,0.00002467712,0.08231961,0.00002323189,0.00005451412,0.00003463129,0.02135377,0.01639722],"study_design_scores_gemma":[0.0003186026,0.00004917304,0.7677658,0.00001353309,0.000002957129,0.000008821737,0.2138666,0.00007870061,0.00007423385,0.00005070051,0.01766743,0.0001034333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974326,0.00009716098,0.000004317869,0.00495777,0.00005753542,0.0001741672,0.0007144724,0.00001056067,0.01965808],"genre_scores_gemma":[0.9984136,0.0009376099,0.0002218045,0.0003641836,0.000002796715,0.000001377953,0.00005591697,3.576744e-7,0.000002408361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.131547,"threshold_uncertainty_score":0.9990031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2020974449310315,"score_gpt":0.3710101998616476,"score_spread":0.1689127549306161,"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."}}