{"id":"W2591144451","doi":"10.71781/10333","title":"Using domain-specific knowledge to improve information retrieval performance","year":2003,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Information retrieval; Computer science; Domain (mathematical analysis); Data science; Data mining; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008354917,0.0003183073,0.0003311694,0.0004487073,0.0003917504,0.001848775,0.001950474,0.0002825984,0.000524379],"category_scores_gemma":[0.00002731788,0.0003055582,0.00008986159,0.001081001,0.00001844817,0.004727419,0.0003073514,0.00040234,0.004723027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002898951,"about_ca_system_score_gemma":0.0007380909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008579078,"about_ca_topic_score_gemma":0.000008938095,"domain_scores_codex":[0.9977819,0.00006010334,0.0007282504,0.0003733248,0.0005944369,0.0004619494],"domain_scores_gemma":[0.9980634,0.00002636916,0.000353835,0.0006841883,0.0006430239,0.0002291717],"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":[0.0003107912,0.00009955048,0.00002516139,0.0001305211,0.00002631799,0.000005463788,0.02170431,0.00005723376,0.004666546,0.002362841,0.001805591,0.9688057],"study_design_scores_gemma":[0.000815472,0.0002861999,0.0008060358,0.0002086564,0.00001593596,0.00001685176,0.001109176,0.00310484,0.06696793,0.00004297262,0.9257886,0.0008373061],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7905465,0.0000698989,0.02702414,0.00004148933,0.003384479,0.002157454,0.00004435944,0.00001306299,0.1767186],"genre_scores_gemma":[0.08091568,0.0001769323,0.7778834,0.0005613774,0.0005561156,0.0001835686,0.00287429,0.0001236858,0.1367249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9679683,"threshold_uncertainty_score":0.9999397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05725414607277895,"score_gpt":0.3401335132496306,"score_spread":0.2828793671768516,"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."}}