{"id":"W1978333903","doi":"10.2481/dsj.1.45","title":"A Mexican case study on a centralised database from world natural history museums","year":2002,"lang":"en","type":"article","venue":"Data Science Journal","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Geography; Ornithology; Natural history; Species richness; Endemism; Database; Collections management; Library science; Ethnology; Ecology; Archaeology; History; Computer science; Biology; Southern Hemisphere","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008370852,0.0001551797,0.0001354755,0.0001131423,0.0006013488,0.0002198819,0.001760869,0.00001633015,0.131237],"category_scores_gemma":[0.0001516482,0.0001240367,0.0000337977,0.0006518186,0.0006603919,0.002014641,0.0008182747,0.0004045866,0.001256145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00198072,"about_ca_system_score_gemma":0.0000297971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00174713,"about_ca_topic_score_gemma":0.003861384,"domain_scores_codex":[0.9974725,0.0000663614,0.0002640958,0.0006007298,0.001092289,0.0005040583],"domain_scores_gemma":[0.9982836,0.0000411567,0.0001526381,0.001065921,0.00001657545,0.000440077],"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":[0.00004856466,0.00219683,0.05145061,0.000001707332,0.00002262252,0.01028361,0.004184839,0.000009404587,0.01292112,0.00009869719,0.9129282,0.005853851],"study_design_scores_gemma":[0.004073201,0.0004499004,0.2677686,0.00005259111,0.0001213675,0.006229777,0.05635346,0.01229061,0.000551304,0.00002645001,0.6508609,0.001221796],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98927,0.0001106885,0.00001421678,0.0005988429,0.001204034,0.0001619935,0.0008938364,0.00003216529,0.007714191],"genre_scores_gemma":[0.9983959,0.00003386961,0.0001157531,0.0008747341,0.0001062098,0.000002591499,0.0001120216,0.000007164209,0.0003517467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2620672,"threshold_uncertainty_score":0.9995215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1465141139966224,"score_gpt":0.3052122749726442,"score_spread":0.1586981609760218,"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."}}