{"id":"W2577984450","doi":"","title":"Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Université Laval","funders":"","keywords":"Ranking (information retrieval); Track (disk drive); Computer science; Construct (python library); Rank (graph theory); Learning to rank; Information retrieval; Artificial intelligence; Search engine; Machine learning; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000412359,0.0001702378,0.0001989366,0.000140906,0.0002398452,0.0001213923,0.0005906677,0.0001272311,0.00008492922],"category_scores_gemma":[0.00008448738,0.0001746306,0.00004630208,0.0004362804,0.0002109624,0.001228693,0.0004577007,0.0001988386,0.0002384375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00030744,"about_ca_system_score_gemma":0.0003861103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000192306,"about_ca_topic_score_gemma":0.0001033663,"domain_scores_codex":[0.9984865,0.0001661326,0.0001620861,0.0003481037,0.0005205019,0.0003166636],"domain_scores_gemma":[0.9986237,0.00007154851,0.0001033152,0.0003212847,0.0004471569,0.000432975],"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":[0.009953924,0.001268633,0.06682982,0.0001719772,0.0002273644,0.00143344,0.07584701,0.0000622807,0.03079139,0.611181,0.05210061,0.1501325],"study_design_scores_gemma":[0.02534395,0.004674927,0.2550485,0.0002334826,0.0001640766,0.0004504733,0.0133861,0.07075951,0.0742152,0.001333609,0.5506396,0.003750496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735859,0.000068965,0.01717332,0.0007455266,0.0002240176,0.0002586113,0.00003526482,0.0002117176,0.007696647],"genre_scores_gemma":[0.9875912,0.00003022585,0.0006440199,0.00003764218,0.00001826071,1.380665e-7,0.00001706916,0.000004375916,0.01165705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6098474,"threshold_uncertainty_score":0.712123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06872451868132005,"score_gpt":0.2774112403357747,"score_spread":0.2086867216544547,"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."}}