{"id":"W2989927096","doi":"10.29311/mas.v17i3.3212","title":"Science and the Language of Natural History Museum Architecture: Problems of Interpretation","year":2019,"lang":"en","type":"article","venue":"Museum and Society","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Arts and Humanities Research Council","keywords":"Interpretation (philosophy); Historicism; Natural (archaeology); Architecture; Expression (computer science); Architecture description language; Natural history; History; Aesthetics; Visual arts; Computer science; Linguistics; Literature; Art; Archaeology; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0003520433,0.00004937814,0.00008803542,0.000009640054,0.00004375617,0.000007446883,0.0001008894,0.00002157082,0.0009043589],"category_scores_gemma":[0.00002478364,0.00003132489,0.00003918025,0.0001203014,0.001261038,0.00007457116,0.0001150806,0.00006367032,0.000005142133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276518,"about_ca_system_score_gemma":0.00001279885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000253483,"about_ca_topic_score_gemma":0.00004525745,"domain_scores_codex":[0.9994767,0.00001678238,0.00008994855,0.0001200416,0.0002048241,0.00009171865],"domain_scores_gemma":[0.9997625,0.0000337317,0.00006398619,0.000103645,0.00001287129,0.00002320254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001116602,0.0001163501,0.01825129,0.0003459374,0.00003834061,3.147432e-7,0.2862158,0.00004911457,0.6695346,0.009489885,0.007596015,0.008250766],"study_design_scores_gemma":[0.005589006,0.0003529023,0.8249079,0.0001327616,0.0000765134,0.00002205528,0.09900933,0.015159,0.01368216,0.0004223445,0.04005693,0.0005891119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927496,0.0005044561,0.00001811756,0.0003687318,0.00009801704,0.0001319535,0.000006292613,0.000007125259,0.006115763],"genre_scores_gemma":[0.9994431,0.00009576449,0.00004115071,0.0002454153,0.00000362542,0.000002635814,0.000002010884,0.000002310947,0.0001640438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8066566,"threshold_uncertainty_score":0.9902096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006667581022149553,"score_gpt":0.2001622666419123,"score_spread":0.1934946856197627,"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."}}