{"id":"W7034760770","doi":"","title":"Using COTS Search Engines and Custom Query Construction at CLEF","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Business, Innovation, and Economy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clef; Query expansion; Query language; Search engine; Web search query; Search engine indexing; Document retrieval; Key (lock)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004232548,0.001445301,0.001474811,0.003093278,0.000901903,0.001574246,0.001381635,0.001288394,0.02069105],"category_scores_gemma":[0.01179484,0.0008845596,0.0008338406,0.003605483,0.0005251231,0.004735796,0.00254662,0.001244815,0.009021524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008599004,"about_ca_system_score_gemma":0.001187512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007443368,"about_ca_topic_score_gemma":0.01083856,"domain_scores_codex":[0.9967525,0.001201269,0.0003743704,0.0007543175,0.0007282773,0.0001892734],"domain_scores_gemma":[0.990505,0.005570057,0.0003709513,0.001723123,0.00147059,0.0003602523],"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":[0.003868221,0.001036028,0.006558688,0.002832467,0.0006409455,0.001059479,0.001251174,0.00540749,0.1385707,0.006262434,0.1144734,0.7180389],"study_design_scores_gemma":[0.003256482,0.002852148,0.02448054,0.0004287648,0.001088631,0.007593312,0.001866974,0.3453711,0.2832061,0.01352172,0.3154344,0.0008997946],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1713422,0.002980724,0.5767654,0.001435711,0.0003965466,0.001912467,0.00989036,0.212118,0.02315854],"genre_scores_gemma":[0.3698014,0.0009948389,0.5630788,0.001411528,0.000275965,0.001037709,0.03911816,0.01000833,0.01427316],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02069105,"threshold_uncertainty_score":0.0692184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05164544261867091,"score_gpt":0.2287187827221573,"score_spread":0.1770733401034864,"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."}}