{"id":"W2923720657","doi":"10.15407/np.46.274","title":"Multicultural Digital Resources of the USA, Canada and Australia","year":2017,"lang":"en","type":"article","venue":"Naukovì pracì Nacìonalʹnoï bìblìoteki Ukraïni ìmenì V Ì Vernadsʹkogo","topic":"Water Resources and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Multiculturalism; Political science; Geography; Media studies; Library science; Telecommunications; Sociology; Computer science; Law","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004053383,0.0003117064,0.000376842,0.00002943642,0.002251643,0.0007285486,0.001689885,0.0001825646,0.000245852],"category_scores_gemma":[0.0010666,0.0002182359,0.0001598194,0.0001344523,0.001277447,0.0008702924,0.0005202254,0.0003926268,0.00002432924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002588979,"about_ca_system_score_gemma":0.000423979,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9478911,"about_ca_topic_score_gemma":0.9587191,"domain_scores_codex":[0.9967499,0.0001351788,0.0004645477,0.0004868112,0.001470807,0.0006927602],"domain_scores_gemma":[0.9975833,0.0001986899,0.000878599,0.0008373002,0.0001932824,0.0003088837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008586192,0.00007041851,0.9711114,0.00004012303,0.0001043338,0.00003707722,0.008017087,0.00002431303,0.0004638081,0.002451433,0.01553935,0.002054854],"study_design_scores_gemma":[0.0005190294,0.00002819879,0.5436071,0.00007015286,0.00002959089,0.000008247508,0.000752557,0.00001007123,0.0005767404,0.00007831744,0.4540611,0.0002589106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859488,0.0002045314,0.00000146975,0.003250096,0.0007430455,0.0003869549,0.0001972103,0.00002031388,0.00924757],"genre_scores_gemma":[0.9597546,0.00008052855,0.00007476336,0.0002138671,0.0004442123,0.00001318728,0.000003339894,0.00002187455,0.03939363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4385217,"threshold_uncertainty_score":0.9990473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289265165853294,"score_gpt":0.2890417187071044,"score_spread":0.260115202121775,"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."}}