{"meta":{"query_hash":"c3da2d887b17","filters":{"venue":"Iranian Journal of Information Processing and Management"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/c3da2d887b17","api":"https://metacan.xera.ac/api/v1/cohort?venue=Iranian+Journal+of+Information+Processing+and+Management"},"results":[{"id":"W3177795256","doi":"10.52547/jipm.36.4.1081","title":"Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval","year":2021,"lang":"en","type":"article","venue":"Iranian Journal of Information Processing and Management","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Toronto Metropolitan University","funders":"","keywords":"Information retrieval; Computer science; Rank (graph theory); Learning to rank; Document clustering; Document retrieval; World Wide Web; Semantic Web; Natural language processing; Artificial intelligence; Ranking (information retrieval); Mathematics","score_opus":0.007069426982035435,"score_gpt":0.27398874291946196,"score_spread":0.2669193159374265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177795256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29556787,0.004062067,0.67147845,0.000898971,0.00031893942,0.0010403377,0.0066763433,0.013138083,0.006818907],"genre_scores_gemma":[0.58223534,0.0004930241,0.40564373,0.00012955649,0.00016696223,0.00037538158,0.0089061735,0.00018653537,0.0018633296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758935,0.00065826013,0.00031639345,0.00036097667,0.00081963127,0.00025540448],"domain_scores_gemma":[0.99587077,0.0018403798,0.00039588922,0.00047388035,0.0012721955,0.00014688399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017432201,0.0011698523,0.0014503498,0.0069097257,0.0007463909,0.0017670316,0.0009525238,0.0014333888,0.0015965399],"category_scores_gemma":[0.010368598,0.00025034472,0.0011752408,0.0047153225,0.0006557595,0.0035326888,0.00089023815,0.0011195878,0.0017650393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007875791,0.0007783089,0.010265295,0.0006782985,0.00011933386,0.00021881289,0.00017348772,0.024357442,0.021914227,0.005179177,0.013802238,0.9217258],"study_design_scores_gemma":[0.00024703465,0.0009354867,0.015230858,0.00010580248,0.00026609044,0.0005160763,0.000459875,0.90488875,0.04987034,0.014878603,0.012451964,0.00014906177],"about_ca_topic_score_codex":0.004968472,"about_ca_topic_score_gemma":0.0064490987,"teacher_disagreement_score":0.0069097257,"about_ca_system_score_codex":0.00090552634,"about_ca_system_score_gemma":0.0013930425,"threshold_uncertainty_score":0.009879112},"labels":[],"label_agreement":null}]}