{"id":"W4414616352","doi":"10.69554/zmqc4019","title":"The AI-powered archivist: Harnessing generative artificial intelligence for streamlined archival description","year":2025,"lang":"en","type":"article","venue":"Journal of digital media management","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Library; Library and Archives Canada","funders":"","keywords":"Sketch; Perplexity; Generative grammar; Interpreter; Curiosity; Project management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01329687,0.0007693216,0.0003866746,0.003506516,0.00234955,0.009579972,0.002587577,0.001144142,0.008300809],"category_scores_gemma":[0.03060561,0.000916739,0.0009984699,0.001947867,0.008511251,0.009349144,0.01194886,0.002655229,0.002629341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408576,"about_ca_system_score_gemma":0.003969201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004376627,"about_ca_topic_score_gemma":0.008910112,"domain_scores_codex":[0.9911446,0.005685913,0.0003303868,0.0007400116,0.001762181,0.0003369156],"domain_scores_gemma":[0.9777255,0.01231503,0.0006447946,0.007182839,0.001300085,0.0008318507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001675917,0.0001531166,0.004729224,0.0008164893,0.00006701347,0.0007480778,0.05527837,0.01105971,0.01007504,0.4156499,0.0197125,0.481543],"study_design_scores_gemma":[0.00005728722,0.0001068646,0.001629966,0.000849508,0.00006338281,0.0009744094,0.01316241,0.05810364,0.01516659,0.3937433,0.5159788,0.0001638015],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0168886,0.0007222095,0.9375699,0.002523736,0.0001462415,0.0003288019,0.0003202428,0.005528835,0.0359715],"genre_scores_gemma":[0.1841656,0.0008547081,0.7938748,0.0005898402,0.00007178157,0.0004054873,0.001029826,0.002962693,0.01604535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329687,"threshold_uncertainty_score":0.07032144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04060310860010026,"score_gpt":0.26687312876575,"score_spread":0.2262700201656498,"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."}}