{"id":"W2762768501","doi":"10.1002/asi.23958","title":"geNov: A new metric for measuring novelty and relevancy in biomedical information retrieval","year":2017,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Novelty; Metric (unit); Computer science; Information retrieval; Ranking (information retrieval); Discriminative model; Redundancy (engineering); Artificial intelligence; Learning to rank; Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004722696,0.00007280188,0.0001592008,0.001444153,0.0007615404,0.0007793274,0.001105694,0.0001385372,3.73033e-7],"category_scores_gemma":[0.01488182,0.00005108954,0.00004415477,0.001364081,0.0001583871,0.01292783,0.000284621,0.0001992571,0.000002015756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003302531,"about_ca_system_score_gemma":0.0006494652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008478693,"about_ca_topic_score_gemma":0.000002541621,"domain_scores_codex":[0.9981636,0.000009876642,0.0006577295,0.00006465127,0.0008603938,0.0002437911],"domain_scores_gemma":[0.9960877,0.0001307972,0.001597633,0.0002305089,0.001871381,0.00008201312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001494368,0.00005250629,0.03040914,0.0001173283,0.00003796396,3.561425e-7,0.004963814,0.00003739606,0.001758795,0.220466,0.002208825,0.7397984],"study_design_scores_gemma":[0.01508718,0.001530853,0.4325821,0.000248656,0.0000777177,0.0002438759,0.00171232,0.09393851,0.02579901,0.0605375,0.3675069,0.0007354037],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.529861,0.0001114303,0.391965,0.07186885,0.002807859,0.002060502,0.00004070195,0.00009291853,0.001191728],"genre_scores_gemma":[0.9883102,0.00005819594,0.0112559,0.0002753806,0.00003650271,0.000006470124,0.000001121745,0.000001638595,0.00005465246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.739063,"threshold_uncertainty_score":0.9934162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02354487229289894,"score_gpt":0.2867085057204873,"score_spread":0.2631636334275884,"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."}}