{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":4,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":4,"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":"8f5650dcc097","filters":{"venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology"}},"results":[{"id":"W4249142469","doi":"10.1109/wi-iat.2012.170","title":"Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentence; Emotion detection; Word (group theory); Affect (linguistics); Context (archaeology); Emotion recognition; Linguistics","authors":[{"name":"Ameeta Agrawal","is_ca":true},{"name":"Aijun An","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06826441657676245,"gpt":0.3050526526364765,"spread":0.236788236059714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005817065,0.0008716388,0.0007002499,0.002575506,0.0004135433,0.001051841,0.0005872219,0.0005987304,0.001430107],"category_scores_gemma":[0.002839187,0.0001981508,0.0008196739,0.001099451,0.0004186248,0.001915235,0.0006600954,0.0008275348,0.001328847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218745,"about_ca_system_score_gemma":0.0003512855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006489392,"about_ca_topic_score_gemma":0.001170421,"domain_scores_codex":[0.9992059,0.0002021526,0.00006440832,0.0002522383,0.0002130946,0.00006220869],"domain_scores_gemma":[0.9983073,0.0008342493,0.0002634351,0.00009886772,0.0004462294,0.00004978421],"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":[0.0005972435,0.0004563916,0.01536377,0.0005379923,0.0001798155,0.0003813333,0.0008158355,0.008512782,0.2228668,0.006142085,0.008146752,0.7359992],"study_design_scores_gemma":[0.00005862729,0.0005739635,0.0553073,0.0001071345,0.0002414057,0.0007921323,0.00105353,0.824524,0.07617557,0.02883994,0.01221585,0.0001105273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2032889,0.001082859,0.7817784,0.0005098345,0.0001926759,0.0004508678,0.001532258,0.003148099,0.008016231],"genre_scores_gemma":[0.7329248,0.0005468011,0.2578757,0.000203784,0.0004329816,0.0005012707,0.00352254,0.0002271546,0.003764997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002575506,"threshold_uncertainty_score":0.004784167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4237211318","doi":"10.1109/wi-iat.2012.122","title":"Verb Oriented Sentiment Classification","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Sentiment analysis; Computer science; Natural language processing; Verb; Sentence; Artificial intelligence; Noun; Feature (linguistics); Computational linguistics; Linguistics","authors":[{"name":"Mostafa Karamibekr","is_ca":true},{"name":"Ali A. Ghorbani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07566559340921215,"gpt":0.3233779520571548,"spread":0.2477123586479427,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217396,0.0009206144,0.0006716733,0.003257697,0.000718598,0.001850598,0.0006010683,0.0006879675,0.007905092],"category_scores_gemma":[0.003161233,0.0001565423,0.0009732475,0.002299459,0.0002383853,0.00115134,0.000594357,0.0006679106,0.005254241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005847563,"about_ca_system_score_gemma":0.0005770279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017776,"about_ca_topic_score_gemma":0.001125295,"domain_scores_codex":[0.9988533,0.0002180708,0.0001397964,0.0001797485,0.0004868822,0.0001221196],"domain_scores_gemma":[0.9986781,0.0002748213,0.0001478413,0.00007951978,0.0007692085,0.00005046767],"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":[0.0004351453,0.0005604029,0.01348524,0.0006935442,0.0001796461,0.0003164292,0.0003737276,0.002127592,0.04271604,0.007517741,0.04952109,0.8820733],"study_design_scores_gemma":[0.0003820025,0.001436036,0.09746773,0.0007234231,0.000777726,0.001996201,0.002472221,0.4631942,0.0850179,0.0528658,0.2934316,0.0002351275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2725258,0.00393248,0.580386,0.00200205,0.003126644,0.006102402,0.02149309,0.006972908,0.1034587],"genre_scores_gemma":[0.5790048,0.002226481,0.3504072,0.0009283944,0.001233325,0.002421357,0.03412649,0.0003852123,0.0292668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007905092,"threshold_uncertainty_score":0.02644515,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2039519793","doi":"10.1109/wi-iat.2012.33","title":"A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"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":"Reinforcement learning; Computer science; Architecture; Task (project management); Set (abstract data type); Artificial intelligence; Quality (philosophy); Hybrid learning; Machine learning; Evolutionary computation; Engineering","authors":[{"name":"Sanjeev Paskaradevan","is_ca":true},{"name":"Jörg Denzinger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05280796709589249,"gpt":0.3403408594984353,"spread":0.2875328924025429,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168906,0.000560108,0.0005381012,0.000534803,0.0004848181,0.0006483287,0.001999511,0.001003612,0.003518891],"category_scores_gemma":[0.002158558,0.0004098528,0.0005590178,0.0003601204,0.000888022,0.001036111,0.001066639,0.001107006,0.0006765001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005735062,"about_ca_system_score_gemma":0.0009195405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003427734,"about_ca_topic_score_gemma":0.00403347,"domain_scores_codex":[0.9993988,0.0001651474,0.00003138677,0.000130009,0.0002248694,0.00004981413],"domain_scores_gemma":[0.9991928,0.0003200851,0.00006134082,0.0001386206,0.0002229875,0.00006419139],"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":[0.0001362133,0.0003139165,0.001556521,0.0001128501,0.0001357904,0.0002222232,0.0004101923,0.5903579,0.01685193,0.04194896,0.001878389,0.3460751],"study_design_scores_gemma":[0.00001069856,0.0000327244,0.00006539773,0.000004499779,0.000007725408,0.00002023934,0.000009075337,0.9942063,0.001203452,0.003679528,0.0007538857,0.000006365993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007980084,0.00003891762,0.9898107,0.00006666423,0.00001591078,0.00005684854,0.000008364083,0.0003879787,0.001634498],"genre_scores_gemma":[0.3402515,0.00007430127,0.6520872,0.0001397135,0.00002357459,0.0003482456,0.00006051272,0.0001015588,0.006913285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003518891,"threshold_uncertainty_score":0.0117718,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983933909","doi":"10.1109/wi-iat.2012.255","title":"A Multi Scale Cognitive Architecture to Account for the Adaptive and Reflective Nature of Behaviour","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Cognitive architecture; Architecture; Cognition; Context (archaeology); Situational ethics; Adaptive behavior; Scale (ratio); Cognitive systems; Human–computer interaction; Cognitive model; Artificial intelligence; Trough (economics); Cognitive science; Psychology; Social psychology","authors":[{"name":"Othalia Larue","is_ca":true},{"name":"Pierre Poirier","is_ca":true},{"name":"Roger Nkambou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06514595017443307,"gpt":0.3457577841187301,"spread":0.280611833944297,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007643153,0.0005697148,0.000350728,0.0005248707,0.0008483325,0.002455593,0.001337055,0.001305634,0.006465404],"category_scores_gemma":[0.002565011,0.0004024777,0.0009369079,0.0004489627,0.001588445,0.002982236,0.001689515,0.001743368,0.0009831308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007827958,"about_ca_system_score_gemma":0.00132287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00422739,"about_ca_topic_score_gemma":0.005010719,"domain_scores_codex":[0.9996276,0.0001189599,0.00001946081,0.0001031275,0.0000905116,0.00004027682],"domain_scores_gemma":[0.9991595,0.0002896904,0.00007513032,0.0002107546,0.0001338637,0.0001310335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000878969,0.0001955661,0.004199429,0.0002364476,0.0002279537,0.0004516589,0.001938189,0.1994155,0.01354457,0.6893688,0.0039999,0.08633414],"study_design_scores_gemma":[0.00004003042,0.0001312873,0.001617827,0.00005402904,0.00009083757,0.0002897235,0.0001859105,0.551766,0.001485064,0.4201087,0.02417958,0.00005093043],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02715457,0.0003350337,0.944399,0.001513176,0.0001787917,0.0001202045,0.0000661795,0.0007873883,0.0254457],"genre_scores_gemma":[0.4905266,0.0003168921,0.4992304,0.0003482403,0.0000745569,0.0002715385,0.00008970844,0.0001060301,0.009036127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006465404,"threshold_uncertainty_score":0.02162892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}