{"id":"W263216679","doi":"","title":"Metis Language & Culture :: Match Some More","year":2006,"lang":"en","type":"article","venue":"","topic":"Linguistics, Language Diversity, and Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Metis; Computer science; Linguistics; Natural language processing; World Wide Web; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004667396,0.0001027369,0.0001170173,0.00004425903,0.0002112629,0.0002158392,0.000142199,0.00003622989,0.006135909],"category_scores_gemma":[0.0001615259,0.00008010917,0.00007988128,0.00001625122,0.000103022,0.0001734056,0.00005865264,0.00007576239,0.0006389884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002035956,"about_ca_system_score_gemma":0.00000703868,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01564898,"about_ca_topic_score_gemma":0.005893096,"domain_scores_codex":[0.9994069,0.00001086288,0.0001085664,0.0001430766,0.0001579207,0.0001726294],"domain_scores_gemma":[0.9994588,0.000008734185,0.00003434203,0.0001482844,0.0003153345,0.00003444947],"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":[0.000003494584,0.00007757097,0.001612565,0.00003109256,0.00002402982,0.00005107698,0.06682964,9.699414e-7,0.00002203847,0.2532144,0.6780329,0.0001002662],"study_design_scores_gemma":[0.0006860158,0.00004424419,0.001855467,0.00002076061,0.0001341189,9.91774e-7,0.2101436,0.00003639757,0.001270002,0.01555514,0.7696583,0.0005949702],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5823874,0.001307588,0.00002781248,0.0001337421,0.008093975,0.0001545426,0.0001944719,0.0003789907,0.4073215],"genre_scores_gemma":[0.708746,0.00001123944,0.0001620657,0.0007564417,0.01985923,0.000001702697,0.00008834437,0.00001138357,0.2703636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2376592,"threshold_uncertainty_score":0.9947726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009173887801269225,"score_gpt":0.2120401374734644,"score_spread":0.2028662496721952,"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."}}