{"id":"W4381333253","doi":"10.12688/openreseurope.16003.1","title":"Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A case study","year":2023,"lang":"en","type":"article","venue":"Open Research Europe","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada; Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Embedding; Newspaper; Python (programming language); Artificial intelligence; Snapshot (computer storage); Natural language processing; Theoretical computer science; Programming language; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001738576,0.0008088455,0.000280681,0.001890184,0.0008175627,0.001641865,0.001617452,0.001807128,0.006757966],"category_scores_gemma":[0.01331069,0.0003649731,0.0009811452,0.001227752,0.001206393,0.002935773,0.002861378,0.001693438,0.002291068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001722043,"about_ca_system_score_gemma":0.001402981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01340182,"about_ca_topic_score_gemma":0.03284075,"domain_scores_codex":[0.9987503,0.0005022723,0.00006114489,0.0003400282,0.0002575087,0.00008858601],"domain_scores_gemma":[0.9908489,0.007213409,0.0002190202,0.0009929482,0.0005396793,0.0001860279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000650411,0.001195681,0.04430851,0.002201339,0.0002319751,0.01668264,0.01177004,0.2157026,0.01854393,0.06477998,0.07104707,0.5528858],"study_design_scores_gemma":[0.00009632414,0.0002106621,0.006440126,0.0003131418,0.0001137133,0.003267069,0.004212084,0.7733777,0.02608608,0.08130825,0.104459,0.0001158721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3996446,0.0008368136,0.5176554,0.005171616,0.000339776,0.0008877821,0.01405757,0.02628833,0.03511809],"genre_scores_gemma":[0.625163,0.0003293436,0.3466705,0.00090591,0.00004272039,0.000273279,0.01633021,0.001433438,0.008851652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01340182,"threshold_uncertainty_score":0.02664769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2018112595288598,"score_gpt":0.4021518027682092,"score_spread":0.2003405432393494,"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."}}