{"id":"W2122869775","doi":"10.1515/mfir.2003.27","title":"Digitization of Cartographic Materials: National Archives of Canada","year":2003,"lang":"en","type":"article","venue":"Microform and Imaging Review","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digitization; Geography; Microform; National archives; Archaeology; Library science; Cartography; History; Computer science; Telecommunications","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":[0.0005608028,0.00004714306,0.0001668774,0.00006009896,0.0001383656,0.00001020962,0.0000453067,0.000007804327,0.00002609473],"category_scores_gemma":[0.0001891035,0.00004086796,0.00002601568,0.0002003208,0.0001590531,0.0001115688,0.000009426655,0.00001651499,2.261919e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020747,"about_ca_system_score_gemma":0.0002389056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04611233,"about_ca_topic_score_gemma":0.01858472,"domain_scores_codex":[0.9993042,0.00005276037,0.0002991374,0.00004945931,0.0001923394,0.0001020442],"domain_scores_gemma":[0.999544,0.00004611498,0.000184543,0.0000410631,0.0001624975,0.00002180719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009167095,0.00007594447,0.3117488,0.02696889,0.0002375531,0.000001356108,0.03013343,0.000001238242,0.007511633,0.5320627,0.03142535,0.05982387],"study_design_scores_gemma":[0.0002319472,0.00000739392,0.01553016,0.003548017,0.00003710928,0.000008247221,0.004220599,0.000001316683,0.003090826,0.002957255,0.970175,0.0001921492],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09068253,0.3328831,0.0003917306,0.002836337,0.0007729582,0.001408439,0.0001599179,0.00004113168,0.5708238],"genre_scores_gemma":[0.9608337,0.03867815,0.0001121931,0.0002141327,0.00001059363,0.000007364723,0.000006232027,0.000002314097,0.0001353518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9387496,"threshold_uncertainty_score":0.9993235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008898645070764825,"score_gpt":0.2493255536316578,"score_spread":0.240426908560893,"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."}}