{"id":"W2104017484","doi":"10.1504/ijeb.2005.007276","title":"Natural language asymmetry and internet infrastructures","year":2005,"lang":"en","type":"article","venue":"International Journal of Electronic Business","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Royal Society of Canada","keywords":"Computer science; The Internet; Information retrieval; Natural language; Identification (biology); Search engine; Natural (archaeology); Information extraction; Natural language processing; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002320713,0.0001195536,0.0001432872,0.0003007753,0.00002090226,0.0002376468,0.001285007,0.00005149337,0.00002086999],"category_scores_gemma":[0.0001650866,0.00009224747,0.00004635794,0.0002258716,0.00004005876,0.001087042,0.0002245652,0.0004038952,0.000002270642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001714285,"about_ca_system_score_gemma":0.0001496423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000251507,"about_ca_topic_score_gemma":0.00002079426,"domain_scores_codex":[0.998934,0.00002627391,0.0002844353,0.000141803,0.0004122302,0.0002012216],"domain_scores_gemma":[0.9988983,0.00005097789,0.0002856681,0.0001176399,0.0006047012,0.00004271864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009286511,0.000068803,0.0005660374,0.00001488622,0.0001998934,0.0001657271,0.001030109,0.00004743921,0.01670043,0.09596162,0.003447765,0.8817044],"study_design_scores_gemma":[0.009012477,0.0009492299,0.05735596,0.001511846,0.00016927,0.04532766,0.000304122,0.08324978,0.4235638,0.2592565,0.1163144,0.002984927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3217802,0.0509191,0.6127808,0.0123354,0.00157288,0.0000883263,0.000002287936,0.0001788643,0.0003421483],"genre_scores_gemma":[0.9110843,0.00007723193,0.08754618,0.0006937227,0.0004737377,6.534538e-7,0.000001637461,0.00000747477,0.0001150386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8787195,"threshold_uncertainty_score":0.3761743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002470699343587297,"score_gpt":0.2501279856897731,"score_spread":0.2476572863461858,"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."}}