{"id":"W19546019","doi":"10.1016/j.jocd.2009.05.001","title":"Language Identification Strategies for Cross Language Information Retrieval.","year":2010,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Language identification; Identification (biology); Task (project management); Natural language; Information retrieval; Grammar; Language model; Metadata; Linguistics; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009779995,0.001863926,0.001785888,0.009635076,0.001895357,0.004741391,0.002602851,0.002372948,0.02395917],"category_scores_gemma":[0.03491482,0.0008524655,0.002161359,0.006169723,0.001133943,0.01092618,0.006036256,0.001579084,0.01467505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329681,"about_ca_system_score_gemma":0.0032381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004930641,"about_ca_topic_score_gemma":0.00478491,"domain_scores_codex":[0.9928274,0.003662678,0.001083138,0.0008947626,0.001128705,0.0004033148],"domain_scores_gemma":[0.9776996,0.01516743,0.0009601621,0.0019941,0.003783825,0.0003948455],"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":[0.0006312827,0.0004036891,0.002480558,0.002154754,0.0002699643,0.001108751,0.002950282,0.00302404,0.01031184,0.02710939,0.04828993,0.9012654],"study_design_scores_gemma":[0.0004853857,0.0006231798,0.00549729,0.00154619,0.001003743,0.004149153,0.01625329,0.3533862,0.04443951,0.3205323,0.2516442,0.0004395297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01427447,0.003044113,0.9435447,0.002317242,0.0003197,0.001925424,0.005051645,0.01936911,0.01015352],"genre_scores_gemma":[0.115384,0.002056438,0.8564451,0.001018501,0.0002439998,0.001727124,0.01432587,0.001133319,0.007665739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02395917,"threshold_uncertainty_score":0.08015138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007840132938790805,"score_gpt":0.3097372937516138,"score_spread":0.301897160812823,"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."}}