{"id":"W3003247616","doi":"10.48550/arxiv.2001.10567","title":"Path Query Data Structures in Practice","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Data structure; Path (computing); Tree traversal; Pointer (user interface); Theoretical computer science; Algorithm; Data mining; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0002592248,0.0002615264,0.0002864787,0.0002040376,0.00008559632,0.0002046393,0.005474264,0.0002230613,0.00001484835],"category_scores_gemma":[0.0001818767,0.0002788232,0.00005674017,0.0006520395,0.0000503037,0.001694076,0.01604916,0.0008813806,0.00004851424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009569494,"about_ca_system_score_gemma":0.0003071343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006583952,"about_ca_topic_score_gemma":0.0000299834,"domain_scores_codex":[0.9975448,0.000220854,0.0001951046,0.001636449,0.0001322878,0.0002705067],"domain_scores_gemma":[0.9961647,0.0001847241,0.0002611395,0.003150584,0.00007964867,0.0001592319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001497379,0.0003071807,0.002550568,0.0002588264,0.0001873046,0.007908707,0.0008340565,0.07266542,0.00004836487,0.8808985,0.02440549,0.009785877],"study_design_scores_gemma":[0.0003699321,0.00002868593,0.001814869,0.0001005836,0.00004161514,0.00001049191,0.0001067813,0.9268715,0.00001237255,0.05378998,0.01642234,0.0004308761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007003622,0.0002584711,0.9880295,0.0006556817,0.0008244168,0.0002554464,0.0001772977,0.0002247457,0.00257077],"genre_scores_gemma":[0.9536348,0.0004819163,0.04482606,0.0004887588,0.000151885,3.661873e-7,0.0002787349,0.00001597962,0.0001215189],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9466311,"threshold_uncertainty_score":0.9999664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1378120292844563,"score_gpt":0.229072452462652,"score_spread":0.09126042317819574,"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."}}