{"id":"W7027183419","doi":"","title":"Building the collections of tomorrow","year":2023,"lang":"en","type":"article","venue":"University of Minnesota Digital Conservancy (University of Minnesota)","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digitization; Cultural heritage; Government (linguistics); Industrial heritage; Digital curation; Cultural heritage management; State (computer science); Field (mathematics); Data curation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006161635,0.0008675208,0.0006128551,0.004420747,0.01767861,0.01438081,0.002947722,0.00171438,0.08956233],"category_scores_gemma":[0.009968806,0.001183666,0.0007909702,0.006467288,0.004528372,0.01411923,0.01812865,0.003241613,0.02074073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01101977,"about_ca_system_score_gemma":0.02947521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3584156,"about_ca_topic_score_gemma":0.6357812,"domain_scores_codex":[0.9971396,0.0004317416,0.0001686323,0.0007084972,0.001096894,0.000454591],"domain_scores_gemma":[0.9917902,0.0004138893,0.0003280024,0.002194949,0.002668005,0.002605015],"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":[0.00005655942,0.00003713094,0.003282834,0.000256159,0.00002230508,0.0001878424,0.01250046,0.00007810636,0.0007591608,0.02841904,0.7898811,0.1645193],"study_design_scores_gemma":[0.000002220325,0.00000425154,0.001331325,0.00008559259,0.000003862413,0.00005385685,0.003016063,0.00001548844,0.0001421928,0.000935484,0.9943984,0.0000112066],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03149405,0.0268295,0.02719153,0.1346841,0.02130339,0.002113888,0.04400692,0.005703276,0.7066733],"genre_scores_gemma":[0.09205478,0.01267843,0.1080983,0.01876748,0.002056191,0.001199375,0.03784912,0.004670667,0.7226257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3584156,"threshold_uncertainty_score":0.7126588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769421434952733,"score_gpt":0.2204434124629303,"score_spread":0.182749198113403,"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."}}