{"id":"W1538415244","doi":"10.46430/phen0016","title":"Output Keywords in Context in an HTML File with Python","year":2012,"lang":"en","type":"article","venue":"The Programming Historian","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Python (programming language); Computer science; World Wide Web; Window (computing); Information retrieval; Context (archaeology); The Internet; Programming language; History","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006740997,0.0001499683,0.0001576784,0.0001474214,0.00008778108,0.0001153263,0.000936193,0.00007514604,0.00001379928],"category_scores_gemma":[0.00005634993,0.0001014778,0.00002345529,0.0004843035,0.00007439679,0.0007939822,0.0001290907,0.0003339107,0.00001265626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003518836,"about_ca_system_score_gemma":0.00007008181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003268392,"about_ca_topic_score_gemma":0.001397087,"domain_scores_codex":[0.9987858,0.00009611789,0.0001838077,0.0002511808,0.0002252859,0.0004578172],"domain_scores_gemma":[0.9991403,0.00006487712,0.00009631275,0.000567153,0.0000420043,0.00008940879],"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":[0.00002647718,0.0001767945,0.003799696,0.00001746112,0.000003346312,0.00002597143,0.03075533,0.000001299428,0.00007785794,0.02418573,0.000725568,0.9402045],"study_design_scores_gemma":[0.001628228,0.001033425,0.004564384,0.0006410478,0.0000228865,0.0001389072,0.001479854,0.002872418,0.002020981,0.01796507,0.9660009,0.001631861],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2260737,0.09867964,0.5985923,0.01193161,0.008799032,0.009377775,0.00003507181,0.01494043,0.0315705],"genre_scores_gemma":[0.7919497,9.078178e-7,0.2064794,0.0001123037,0.0001376148,0.00008790511,0.00000442493,0.00001734411,0.001210309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9652753,"threshold_uncertainty_score":0.4138143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676402549969366,"score_gpt":0.2582049743164332,"score_spread":0.2414409488167395,"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."}}