{"id":"W3046679967","doi":"10.1515/pdtc-2020-0020","title":"The No-Nonsense Guide to Born-Digital Content","year":2020,"lang":"en","type":"article","venue":"Preservation Digital Technology & Culture","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Nonsense; Content (measure theory); Computer science; Psychology; Mathematics; Biology; Genetics","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002581363,0.0009753703,0.0006290298,0.003982209,0.002917933,0.01016434,0.002034845,0.003820474,0.09152804],"category_scores_gemma":[0.00887164,0.001050272,0.0005029986,0.002936955,0.00564668,0.01255062,0.004409832,0.005527453,0.08072505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002628047,"about_ca_system_score_gemma":0.005167278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006506254,"about_ca_topic_score_gemma":0.02054375,"domain_scores_codex":[0.9975297,0.0007348618,0.0002566449,0.0001554344,0.001179469,0.0001438938],"domain_scores_gemma":[0.9950038,0.002278397,0.0001313392,0.0005611355,0.001555217,0.0004701326],"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":[0.00001028612,0.00003152541,0.0001671741,0.000174237,0.00000200659,0.000104124,0.00134392,0.00007582978,0.0003294093,0.1670677,0.6999919,0.130702],"study_design_scores_gemma":[0.000001046123,0.000003145498,0.00005542533,0.0001533806,6.504811e-7,0.00008647696,0.000254103,0.00004588567,0.00007851299,0.01051516,0.9888025,0.000003716737],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0007298857,0.0339337,0.06423721,0.03931357,0.00719839,0.0002210622,0.001662013,0.00379647,0.8489078],"genre_scores_gemma":[0.00672533,0.02383303,0.03447629,0.01080529,0.001692075,0.0002867782,0.0009492203,0.002344066,0.918888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9898357,"threshold_uncertainty_score":0.3061918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07269122164497818,"score_gpt":0.2456024662890759,"score_spread":0.1729112446440977,"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."}}