{"id":"W2966733191","doi":"10.3897/biss.3.35887","title":"Bio-logging Data in Darwin Core: Use Cases","year":2019,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ocean Tracking Network","funders":"","keywords":"Darwin (ADL); Computer science; Data science; Tracking (education); Biodiversity; Logging; Core (optical fiber); Documentation; Wearable computer; Health informatics tools; Global Positioning System; Data mining; Informatics; Geography; Ecology; Telecommunications; Software engineering; Engineering","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":[0.0008568272,0.00006754843,0.00006835114,0.0001543999,0.000194485,0.000159987,0.0003206145,0.00005858624,0.00006074547],"category_scores_gemma":[0.0004441239,0.00006509166,0.00001128627,0.0002434515,0.0001945715,0.0002814421,0.0004900487,0.00004443437,0.00002929944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004080519,"about_ca_system_score_gemma":0.0002838104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009256388,"about_ca_topic_score_gemma":0.0000268157,"domain_scores_codex":[0.9989761,0.00001011091,0.0001085737,0.0001802371,0.0005802321,0.0001448084],"domain_scores_gemma":[0.999205,0.00001002013,0.00005759238,0.0003165521,0.0003480934,0.00006273008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001101922,0.000009122389,0.9715106,0.00002870972,0.000007645985,0.000002574561,0.0007837291,0.000110871,0.003455924,0.0001119638,0.01801275,0.005855867],"study_design_scores_gemma":[0.0009836464,0.000113183,0.3860078,0.0000101036,0.000008889557,0.00002294481,0.002215976,0.0003869144,0.001789247,0.000005723951,0.6082178,0.0002377575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969023,0.00002096772,0.00008483639,0.00009693782,0.0001545675,0.0001157682,0.001909719,0.000006324665,0.0007085588],"genre_scores_gemma":[0.99882,0.00006661223,0.0002161025,0.0004611959,0.0000067826,1.633037e-7,0.0004089124,4.477531e-7,0.00001976438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5902051,"threshold_uncertainty_score":0.2654361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04094223791203304,"score_gpt":0.2824345051589379,"score_spread":0.2414922672469048,"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."}}