{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"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":"c635886ce359","filters":{"venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on"}},"results":[{"id":"W2707606089","doi":"","title":"Expression, pose, and illumination invariant face recognition using lower order pseudo Zernike moments","year":2015,"lang":"en","type":"article","venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Invariant (physics); Normalization (sociology); Facial expression; Computer vision; Facial recognition system; Computer science; Gabor wavelet; Wavelet; Expression (computer science); Orientation (vector space); Face (sociological concept); Mathematics; Wavelet transform; Discrete wavelet transform; Geometry; Optics; Physics","authors":[{"name":"Madeena Sultana","is_ca":true},{"name":"Marina L. Gavrilova","is_ca":true},{"name":"Svetlana Yanushkevich","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04528622607631284,"gpt":0.3086133397257378,"spread":0.263327113649425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002442505,0.0004119235,0.0006635199,0.0009146346,0.000175155,0.0004205113,0.0005390851,0.0002838254,0.001129189],"category_scores_gemma":[0.0008139652,0.0001604941,0.0006126766,0.0007628956,0.0002494301,0.000839208,0.0004074733,0.0004557352,0.0006840993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002330258,"about_ca_system_score_gemma":0.0002799424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007729498,"about_ca_topic_score_gemma":0.0009435279,"domain_scores_codex":[0.9996099,0.00003491612,0.00001578064,0.00006584931,0.0002337533,0.00003982191],"domain_scores_gemma":[0.9997585,0.00004655679,0.00004324986,0.00004552339,0.00009261079,0.00001345598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000173054,0.00008567503,0.001765847,0.0001625732,0.00005073929,0.0001978384,0.00005974702,0.007618202,0.3163429,0.003253142,0.002568645,0.6677217],"study_design_scores_gemma":[0.00003931464,0.0006152089,0.03110243,0.00004178103,0.0001690487,0.003356765,0.0002120333,0.5386605,0.4043706,0.00651342,0.01474946,0.0001693623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09019531,0.0009406412,0.9039527,0.0001756,0.000207134,0.00008827229,0.000287543,0.001129775,0.003023028],"genre_scores_gemma":[0.6070007,0.001738547,0.3835183,0.0001473169,0.0001797621,0.0001350223,0.001062671,0.0001928939,0.006024716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001129189,"threshold_uncertainty_score":0.003777504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2631558258","doi":"","title":"High Definition visual attention based video summarization","year":2015,"lang":"en","type":"article","venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Automatic summarization; Artificial intelligence; Computer science; Frame (networking); Key frame; Feature (linguistics); Shot (pellet); Histogram; Computer vision; Construct (python library); Video tracking; Visualization; Pattern recognition (psychology); Reference frame; Histogram of oriented gradients; Block-matching algorithm; Key (lock); Video processing; Image (mathematics)","authors":[{"name":"Yiming Qian","is_ca":true},{"name":"Matthew Kyan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02894511363994841,"gpt":0.2952617320729818,"spread":0.2663166184330334,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008674836,0.001300443,0.001067772,0.003323511,0.0005116286,0.001253173,0.001145537,0.0006042747,0.00281034],"category_scores_gemma":[0.003189287,0.0002665783,0.0007470901,0.001818114,0.0002971851,0.001638614,0.001095462,0.0007140675,0.001318937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629854,"about_ca_system_score_gemma":0.0005754777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00297472,"about_ca_topic_score_gemma":0.003269736,"domain_scores_codex":[0.9990315,0.0001316871,0.00007776814,0.0003071943,0.000352284,0.0000995463],"domain_scores_gemma":[0.9982873,0.0003093459,0.000185467,0.0001568427,0.0009823045,0.00007885902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004297494,0.0001151608,0.0009414813,0.0004123797,0.00009261732,0.0001657443,0.0002649153,0.009691875,0.07744767,0.002396545,0.008457908,0.899584],"study_design_scores_gemma":[0.0001290704,0.001376466,0.01597473,0.0001170113,0.0004384238,0.001037409,0.0006496911,0.724071,0.2070919,0.01054071,0.03842187,0.0001518808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02868538,0.001567171,0.9614403,0.00015713,0.0001880019,0.0003454611,0.0006629456,0.004478228,0.002475465],"genre_scores_gemma":[0.2968,0.001086567,0.6896783,0.0001592876,0.0003419986,0.000355367,0.004164517,0.0004395782,0.006974556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003323511,"threshold_uncertainty_score":0.0094015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}