{"id":"W2285348533","doi":"10.16995/dm.52","title":"Reading: Exploration of a Large Database of French Notarial Acts with Social Network Methods","year":2013,"lang":"en","type":"article","venue":"Digital Medievalist","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Relational database; Consistency (knowledge bases); Set (abstract data type); Information retrieval; DECIPHER; Relational model; Relation (database); Variety (cybernetics); Data mining; Visualization; Data science; Artificial intelligence; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003375097,0.0001038952,0.0002140001,0.00006052934,0.00005249502,0.0001744315,0.0004098525,0.00003912238,0.00002585827],"category_scores_gemma":[0.0002483509,0.00008416967,0.00003942798,0.0004976233,0.00007948788,0.002658986,0.0002019477,0.00005622856,0.00001354059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001234052,"about_ca_system_score_gemma":0.00007218918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003672004,"about_ca_topic_score_gemma":0.00001095376,"domain_scores_codex":[0.9988818,0.00006484095,0.0003084747,0.0002051825,0.0003462385,0.0001935228],"domain_scores_gemma":[0.9990082,0.000109894,0.0002395791,0.0003150043,0.0002508628,0.00007650758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005061126,0.000854213,0.002252524,0.0002783733,0.0001960761,0.00001079051,0.006738453,0.0001628342,0.002304194,0.8890806,0.05844068,0.03963058],"study_design_scores_gemma":[0.01012407,0.002289762,0.00762834,0.001237962,0.0003196781,0.00002793654,0.00253958,0.629505,0.03723509,0.07615966,0.230028,0.002904895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001501385,0.00001113761,0.992558,0.0002884432,0.0002229589,0.0001427186,0.0001207467,0.0000422695,0.00511234],"genre_scores_gemma":[0.8935086,0.000008214884,0.1047193,0.0002337905,0.0003718462,0.00001199061,0.000807637,0.00001700165,0.0003215467],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8920072,"threshold_uncertainty_score":0.3432339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04974956432205806,"score_gpt":0.3537140026639678,"score_spread":0.3039644383419097,"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."}}