{"id":"W2968817071","doi":"10.3897/biss.3.35243","title":"Quantifying Institutional Reach Through the Human Network in Natural History Collections","year":2019,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Identifier; Digitization; Computer science; World Wide Web; Natural (archaeology); Unique identifier; Library science; Data science; Internet privacy; History; Telecommunications; Archaeology","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006245457,0.0003601947,0.0003075982,0.006277599,0.001258214,0.004772823,0.001092998,0.0008702828,0.006472854],"category_scores_gemma":[0.03502599,0.0003092141,0.0003690256,0.008304383,0.001686483,0.007516107,0.006135899,0.0008022965,0.002096511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001607006,"about_ca_system_score_gemma":0.001128591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058898,"about_ca_topic_score_gemma":0.02330629,"domain_scores_codex":[0.9938764,0.00256378,0.0004024926,0.00134579,0.001418356,0.0003932885],"domain_scores_gemma":[0.9797832,0.009551234,0.004355904,0.002609221,0.002683111,0.001017275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002753894,0.0001300008,0.7139011,0.001380468,0.0003060564,0.0003472849,0.02236783,0.01118168,0.001891803,0.06171481,0.02196136,0.1645422],"study_design_scores_gemma":[0.00003758588,0.0002144803,0.5730716,0.001793662,0.0002629009,0.0008968378,0.04764429,0.03847186,0.005141106,0.07175697,0.2605421,0.0001666416],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7427145,0.003768825,0.08823518,0.003119129,0.0002494933,0.0006928149,0.03992489,0.001273569,0.1200216],"genre_scores_gemma":[0.920867,0.0009542376,0.05350808,0.0002732529,0.0001032218,0.0006302655,0.0145581,0.0003702009,0.008735587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9937224,"threshold_uncertainty_score":0.0330295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04490553062742363,"score_gpt":0.2661966924879287,"score_spread":0.2212911618605051,"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."}}