{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007191244,0.001812208,0.0008687257,0.0009474209,0.0005434119,0.001503936,0.001811611,0.001080828,0.1734471],"category_scores_gemma":[0.005075212,0.0008555236,0.001239797,0.001032204,0.0004533168,0.002851467,0.003095024,0.001771268,0.09706583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000651624,"about_ca_system_score_gemma":0.001186837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001741857,"about_ca_topic_score_gemma":0.001888777,"domain_scores_codex":[0.9994866,0.00004745625,0.00004989315,0.0001426976,0.0001940812,0.00007918797],"domain_scores_gemma":[0.9987373,0.0004862331,0.00008783867,0.000236265,0.0003424578,0.0001099835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001047547,0.00020611,0.002587646,0.001615212,0.00009775593,0.0007285498,0.0004817505,0.002331949,0.009779706,0.009042093,0.8103951,0.1616866],"study_design_scores_gemma":[0.0005976924,0.0001910314,0.005561845,0.0005242219,0.0001065402,0.0007965481,0.0004003596,0.04204975,0.07323878,0.05773161,0.8185299,0.0002717738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.003820717,0.000110917,0.194209,0.0007763094,0.0004452692,0.0002813937,0.06595314,0.7079783,0.026425],"genre_scores_gemma":[0.1017181,0.0006716607,0.3342917,0.002434361,0.0003465729,0.001792105,0.1259619,0.3440243,0.08875924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1734471,"threshold_uncertainty_score":0.5802383,"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."}}