{"id":"W2006536984","doi":"10.1145/1183614.1183737","title":"Constructing better document and query models with markov chains","year":2006,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Markov chain; Information retrieval; Query optimization; Theoretical computer science; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004014495,0.001115609,0.001886356,0.001732373,0.0006583909,0.00175854,0.001672044,0.002287612,0.002156088],"category_scores_gemma":[0.01443421,0.001059883,0.001965308,0.00231433,0.0007640757,0.005609999,0.001491084,0.003044946,0.00113129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503619,"about_ca_system_score_gemma":0.002200092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0123401,"about_ca_topic_score_gemma":0.01253711,"domain_scores_codex":[0.9974734,0.001149564,0.0001655599,0.0005589629,0.0004570462,0.0001953818],"domain_scores_gemma":[0.9890577,0.008525165,0.0004469016,0.001134754,0.0006225585,0.0002129154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001249511,0.0005779683,0.004608484,0.0002896603,0.0002843215,0.0003158285,0.0006440251,0.6845839,0.01688179,0.02598823,0.006515213,0.2580611],"study_design_scores_gemma":[0.00003506385,0.00003356055,0.0001352208,0.000005337182,0.00001633109,0.00002736783,0.00001458174,0.9933373,0.00110538,0.004752078,0.0005241194,0.00001369521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04348761,0.0004466368,0.952536,0.0003465103,0.00003711313,0.0001439254,0.000316398,0.002016303,0.0006695554],"genre_scores_gemma":[0.3883906,0.0008350834,0.6017128,0.0005279831,0.0002831523,0.0005388111,0.003122356,0.0005008819,0.004088428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0123401,"threshold_uncertainty_score":0.02453655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006209587728475849,"score_gpt":0.1883046058453408,"score_spread":0.182095018116865,"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."}}