{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01271836,0.0007734029,0.0005678691,0.002502456,0.001249932,0.002758962,0.001955491,0.002232602,0.002216138],"category_scores_gemma":[0.03082977,0.0007267116,0.0009039802,0.003542963,0.001414024,0.004297878,0.003950652,0.001187123,0.001290499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344536,"about_ca_system_score_gemma":0.0009123042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008453449,"about_ca_topic_score_gemma":0.01357286,"domain_scores_codex":[0.9903915,0.002258921,0.001326551,0.0009094979,0.004696466,0.0004170172],"domain_scores_gemma":[0.9773367,0.01338825,0.0009075782,0.005086179,0.002749695,0.0005315366],"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":[0.003039998,0.001354379,0.236631,0.004053479,0.0004918193,0.0219058,0.02825877,0.04033991,0.03044285,0.05914408,0.3113818,0.2629562],"study_design_scores_gemma":[0.0003804887,0.000548243,0.09303437,0.001262519,0.0001948658,0.007514652,0.009692646,0.1469159,0.07035586,0.02012245,0.6497203,0.0002576699],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.675844,0.0009593982,0.1453571,0.007468336,0.0003882454,0.002810662,0.07725421,0.03965215,0.05026595],"genre_scores_gemma":[0.6807396,0.0008376828,0.2320823,0.001478774,0.0001184183,0.001973792,0.06589621,0.007314951,0.009558294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01271836,"threshold_uncertainty_score":0.06726193,"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."}}