{"meta":{"query_hash":"0f879265da3d","filters":{"venue":"International Conference on Intelligent Information Processing"},"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/0f879265da3d","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Conference+on+Intelligent+Information+Processing"},"results":[{"id":"W2294110607","doi":"","title":"Adaptive Identification of Landmine Class by Evaluating the Total Degree of Conformity of Ring-CSOM Weights in a Ground Penetrating Radar System","year":2010,"lang":"en","type":"article","venue":"International Conference on Intelligent Information Processing","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":6,"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 Saskatchewan","funders":"","keywords":"Ground-penetrating radar; Radar; Artificial intelligence; Feature (linguistics); Identification (biology); Remote sensing; Computer science; Feature extraction; Class (philosophy); Feature vector; Computer vision; Noise (video); Geology; Pattern recognition (psychology); Image (mathematics)","score_opus":0.05922126072996002,"score_gpt":0.3256434527137927,"score_spread":0.26642219198383266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294110607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9248304,0.000014781231,0.06608413,0.000050996296,0.00020554182,0.00027872054,0.00006623668,0.000029022574,0.00844021],"genre_scores_gemma":[0.9973443,0.000005472598,0.002500143,0.000005091265,0.000023761224,0.000044650697,0.00005083876,0.000005903343,0.000019851032],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841535,0.000025284204,0.00096864445,0.000085875006,0.00040440634,0.00010043015],"domain_scores_gemma":[0.9985168,0.00014472863,0.0005817282,0.00014243198,0.0005889551,0.000025361363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005375376,0.00011463052,0.00017193485,0.0001389452,0.000045689936,0.00005214415,0.00027274236,0.000064260086,0.00002149854],"category_scores_gemma":[0.000111966714,0.000092182396,0.000046798985,0.00019131768,0.00007617541,0.00053882727,0.000028001392,0.00023505067,0.0000053852073],"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":[0.00009024868,0.0001020367,0.00024586855,0.0006408244,0.000056724588,8.8052055e-8,0.0043112473,0.004366912,0.54264086,0.24118657,0.0000065103036,0.2063521],"study_design_scores_gemma":[0.0002661869,0.000087582535,0.0067201834,0.00044960782,0.0000147246465,0.000003915433,0.00280144,0.83853185,0.14913815,0.0018147427,0.000034595017,0.00013702942],"about_ca_topic_score_codex":0.0000709033,"about_ca_topic_score_gemma":0.000011161627,"teacher_disagreement_score":0.8341649,"about_ca_system_score_codex":0.000048829523,"about_ca_system_score_gemma":0.00005983022,"threshold_uncertainty_score":0.37590888},"labels":[],"label_agreement":null}]}