{"id":"W1999812853","doi":"10.1145/1866480.1866484","title":"LTIX","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Joins; XML; Search engine indexing; Path (computing); Pruning; Path expression; Data mining; Scheme (mathematics); Index (typography); XPath; Tree (set theory); Tree structure; Twig; Data structure; Information retrieval; Theoretical computer science; XML database; Query language; Mathematics","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":[0.00005168857,0.00002728738,0.00003161554,0.00001319366,0.00003471199,0.00001600741,0.0001596962,0.000011936,0.00005829312],"category_scores_gemma":[0.00001304599,0.00002103876,0.00001079791,0.00005899067,0.00001246804,0.0003678099,0.00008891777,0.0000516783,0.0002115994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":9.494453e-7,"about_ca_system_score_gemma":0.000009687813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002243229,"about_ca_topic_score_gemma":0.00006649269,"domain_scores_codex":[0.9997234,0.000003020742,0.00004636861,0.00009955673,0.00005355828,0.00007406344],"domain_scores_gemma":[0.9995815,0.000012092,0.00001067012,0.0003478221,0.00001537909,0.00003259566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[8.569634e-8,0.000002833991,0.00006916193,7.601537e-7,4.363633e-7,0.000001761687,0.00002172802,3.327862e-7,0.006247467,0.971379,0.0009915645,0.02128487],"study_design_scores_gemma":[0.00005386658,0.000007564048,0.001130854,0.000001148855,1.783288e-7,0.00001952284,0.000007086348,0.001463252,0.01115472,0.002298088,0.9837916,0.00007205342],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004245989,0.000004915243,0.9633618,0.0003088702,0.0005115086,0.0000217123,6.810296e-7,0.0001408621,0.03140367],"genre_scores_gemma":[0.2495771,7.659716e-7,0.7474391,0.0002629468,0.00007535073,0.000003795444,7.147867e-7,0.000001992413,0.002638268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9828001,"threshold_uncertainty_score":0.2719752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004661640029444121,"score_gpt":0.2231415807463154,"score_spread":0.2184799407168713,"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."}}