{"id":"W7110020821","doi":"10.4230/lipics.cpm.2025.19","title":"The Trie Measure, Revisited","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Trie; Cardinality (data modeling); Monotone polygon; Encoding (memory); Binary number; Integer (computer science); Sequence (biology); Binary logarithm; Focus (optics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001045372,0.0003101474,0.000336252,0.0001871378,0.001069387,0.001103186,0.002511284,0.000160577,0.000007816479],"category_scores_gemma":[0.0002371975,0.0002044493,0.0002185129,0.0006677856,0.0001080035,0.001637564,0.001020837,0.0003810802,0.0001050747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009278808,"about_ca_system_score_gemma":0.0001442258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009867402,"about_ca_topic_score_gemma":0.000005102346,"domain_scores_codex":[0.9975126,0.00005862711,0.0009540161,0.0002869199,0.0005274227,0.0006603544],"domain_scores_gemma":[0.9972478,0.0003476584,0.0003140528,0.001567718,0.0003824631,0.0001403429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001622575,0.0002577351,0.001169208,0.0003862287,0.0002857144,0.000005999424,0.002056135,0.00004963343,0.00005697953,0.3185587,0.2286829,0.4483284],"study_design_scores_gemma":[0.001840926,0.00008494594,0.000593991,0.0002508307,0.00002414737,0.00001372928,0.0001962127,0.1348946,0.0005202171,0.005027633,0.8562262,0.0003265318],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001331385,0.0005494065,0.9761959,0.001628113,0.001718224,0.00108507,0.0001939936,0.0003308798,0.01696702],"genre_scores_gemma":[0.6011633,0.001927849,0.3495975,0.02441834,0.001390647,0.001335438,0.002086295,0.0002079024,0.01787275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6275433,"threshold_uncertainty_score":0.9999338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00988895079960905,"score_gpt":0.2593516950114112,"score_spread":0.2494627442118021,"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."}}