{"id":"W4384106200","doi":"10.20368/1971-8829/729","title":"Using information retrieval to detect conﬂicting questions","year":2012,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Information retrieval; Computer science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002560406,0.0002241941,0.0004377535,0.0009276282,0.0005031607,0.002469612,0.002792635,0.0001043708,0.0002486868],"category_scores_gemma":[0.000733232,0.0002089875,0.0001177423,0.001610237,0.00003541634,0.01180761,0.001124718,0.0002971902,0.00006371512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001909377,"about_ca_system_score_gemma":0.0001335084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008362902,"about_ca_topic_score_gemma":0.000009269872,"domain_scores_codex":[0.9974391,0.0002701683,0.0008584296,0.0002285319,0.000693074,0.0005106631],"domain_scores_gemma":[0.9976837,0.0002621982,0.0007404924,0.0005558674,0.0003496503,0.0004080773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001573127,0.0002350821,0.4976716,0.0002189302,0.0002870898,0.00001812903,0.0105486,0.002931708,0.3392553,0.004621283,0.03501508,0.1090399],"study_design_scores_gemma":[0.001486792,0.00006737886,0.6182008,0.003036712,0.0001214655,0.0003489874,0.0007877822,0.01853599,0.2431768,0.003942044,0.1078007,0.002494474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6140097,0.004685139,0.3716185,0.0001953179,0.003289162,0.0005764876,0.00001203698,0.0001640461,0.005449583],"genre_scores_gemma":[0.9867532,0.0001874759,0.01229776,0.000340146,0.0003326998,0.00001058719,0.000001969299,0.00001758127,0.00005854058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3727435,"threshold_uncertainty_score":0.9985659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3386060099135738,"score_gpt":0.5623113108531108,"score_spread":0.223705300939537,"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."}}