{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001011606,0.00005688917,0.00005798755,0.00005055489,0.0011625,0.0001067654,0.000189191,0.00002711408,0.005180717],"category_scores_gemma":[0.00007154287,0.00004353784,0.00001844847,0.0007439918,0.0006390116,0.002336564,0.0001717627,0.00009702161,0.0003158697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003486021,"about_ca_system_score_gemma":0.0001384727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005727468,"about_ca_topic_score_gemma":0.0004804866,"domain_scores_codex":[0.998673,0.00001388988,0.000118057,0.0001023079,0.0009127924,0.0001799717],"domain_scores_gemma":[0.9996964,0.00001194983,0.00005730656,0.0001001841,0.0001004335,0.00003368637],"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":[0.00004695251,0.00003339295,0.2697726,0.00001500564,0.000005185454,0.000001193102,0.01124168,0.0007743264,0.0004835111,0.0287109,0.6880788,0.0008364973],"study_design_scores_gemma":[0.0002904997,0.00001835723,0.2068589,0.000003891098,0.000002084005,0.000004081125,0.004603237,0.0002103366,0.0000232294,0.00001987358,0.7878848,0.00008071524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.81764,0.00003612403,0.00003063957,0.0004313197,0.0004245415,0.0001642751,0.0001201185,0.00002041975,0.1811326],"genre_scores_gemma":[0.9990031,0.00003120875,0.00001872613,0.0008166661,0.000005084037,0.000002021558,0.00002129108,2.941584e-7,0.0001016468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1813631,"threshold_uncertainty_score":0.9957287,"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."}}