{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"a17da8325c9e","filters":{"venue":"International Conference on Pervasive and Embedded Computing and Communication Systems"}},"results":[{"id":"W2750709342","doi":"","title":"Sea Ice SAR Segmentation using a Novel Markov Random Field Model with S-KPFD Fast Classification.","year":2017,"lang":"en","type":"article","venue":"International Conference on Pervasive and Embedded Computing and Communication Systems","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Markov random field; Segmentation; Computer science; Sea ice; Hidden Markov model; Artificial intelligence; Synthetic aperture radar; Image segmentation; Pattern recognition (psychology); Markov chain; Markov model; Field (mathematics); Geology; Remote sensing; Climatology; Machine learning; Mathematics","authors":[{"name":"Yingying Kong","is_ca":false},{"name":"Henry Leung","is_ca":true},{"name":"Shiyu Xing","is_ca":true},{"name":"Da Lu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07279971996731623,"gpt":0.3060021586900173,"spread":0.2332024387227011,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005360155,0.0004738403,0.0006895731,0.0007021126,0.0002457265,0.0005755339,0.0009802581,0.0008414048,0.001424472],"category_scores_gemma":[0.001018917,0.0003522787,0.0006644011,0.000545541,0.0002341238,0.0009257332,0.0004480951,0.0006942347,0.0007740603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003536832,"about_ca_system_score_gemma":0.0008020787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009099253,"about_ca_topic_score_gemma":0.01245748,"domain_scores_codex":[0.9998436,0.00003182231,0.00000977084,0.00004338425,0.00003979629,0.00003161938],"domain_scores_gemma":[0.9996548,0.0001549317,0.00003710858,0.00003551074,0.00009394254,0.00002384852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004728891,0.0001562163,0.003148185,0.00009897661,0.00009669756,0.0001202412,0.00005964203,0.6202844,0.01268044,0.004458804,0.005949056,0.3524744],"study_design_scores_gemma":[0.000002758247,0.000008695528,0.0001949302,0.000002129084,0.000004449759,0.000010953,0.000002277399,0.9984961,0.000537664,0.0004886697,0.0002483253,0.000003007752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03153836,0.0005035157,0.9651203,0.0002630824,0.00009978591,0.00004477562,0.0002611519,0.0007515503,0.001417518],"genre_scores_gemma":[0.6437945,0.0006336981,0.3462656,0.0003168516,0.0001877343,0.0001364896,0.001562734,0.000192279,0.006910081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009099253,"threshold_uncertainty_score":0.01809257,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}