{"id":"W2373380784","doi":"","title":"York University at CLEF eHealth 2015: Medical Document Retrieval","year":2015,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Clef; Information retrieval; Computer science; eHealth; Relevance (law); Task (project management); World Wide Web; Normalization (sociology); Document retrieval; Query expansion; Information access; Health care","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001043864,0.00007997263,0.0001043044,0.00008262043,0.0001478813,0.00006277495,0.0008110563,0.00008622501,0.0005373405],"category_scores_gemma":[0.00007480017,0.00006484404,0.00004234043,0.0004250578,0.00005706732,0.0005642765,0.0005834984,0.0001509348,0.001372189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003698736,"about_ca_system_score_gemma":0.0006004121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001525872,"about_ca_topic_score_gemma":0.00003106546,"domain_scores_codex":[0.9980591,0.00008449948,0.000174963,0.0001758429,0.001210013,0.0002956164],"domain_scores_gemma":[0.998682,0.00004257186,0.00005052143,0.0003175207,0.0001584417,0.0007489153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003488733,0.0002013311,0.004130246,0.00002960394,0.00002323217,0.0002355726,0.00482306,0.00002389625,0.0000286511,0.4391487,0.5187957,0.03221119],"study_design_scores_gemma":[0.002920857,0.0006329302,0.006196572,0.00002000971,0.000007765443,0.0001055164,0.0006327674,0.02260296,0.00151368,0.001113149,0.9638026,0.0004511524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3530279,0.0001462062,0.4226955,0.05691034,0.003046304,0.0009306963,0.00001288935,0.001358241,0.161872],"genre_scores_gemma":[0.886437,0.0000786336,0.02470339,0.004664941,0.0002043363,9.585336e-7,0.00002320083,0.00001095295,0.08387659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5334091,"threshold_uncertainty_score":0.9994054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03629037255503566,"score_gpt":0.2925375850030408,"score_spread":0.2562472124480052,"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."}}