{"id":"W6887792168","doi":"10.17613/nn682-wy802","title":"Bootstrapping a historical commodities lexicon with SKOS and DBpedia","year":2014,"lang":"en","type":"other","venue":"Knowledge Commons (Lakehead University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Lexicon; Bootstrapping (finance); Semantic Web; Simple Knowledge Organization System; Visualization; Term (time); Word (group theory); Linked data; Semantics (computer science)","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":[],"consensus_categories":[],"category_scores_codex":[0.001922813,0.001093182,0.0007403571,0.01282744,0.001521072,0.004638921,0.001554524,0.0007319347,0.00795073],"category_scores_gemma":[0.01332354,0.0008876793,0.001191547,0.007737237,0.0009059768,0.00728727,0.004209181,0.001947037,0.008999558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252645,"about_ca_system_score_gemma":0.002149862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01180299,"about_ca_topic_score_gemma":0.02524966,"domain_scores_codex":[0.9983675,0.0003901905,0.0001953162,0.0005277166,0.0004122429,0.0001070418],"domain_scores_gemma":[0.9940349,0.002417858,0.0002688648,0.001623979,0.001367216,0.0002871374],"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":[0.0006104925,0.0007201899,0.01510137,0.001811074,0.0003304592,0.002559092,0.003305596,0.01335728,0.01962954,0.03278277,0.2578749,0.6519173],"study_design_scores_gemma":[0.0001360101,0.0001223355,0.01256008,0.000720872,0.0003004001,0.001515712,0.006055955,0.2169685,0.03071823,0.05255171,0.678103,0.0002471757],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08818454,0.0009541968,0.585928,0.001829871,0.001185157,0.00138379,0.1688682,0.07580673,0.0758596],"genre_scores_gemma":[0.1777104,0.0007416008,0.5483343,0.0004177715,0.0002041504,0.0006104872,0.2590412,0.005002346,0.007937825],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01282744,"threshold_uncertainty_score":0.0265978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02895249683292651,"score_gpt":0.2190552663942301,"score_spread":0.1901027695613036,"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."}}