{"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":"04cb2815c728","filters":{"venue":"Computing and Informatics"}},"results":[{"id":"W4389493777","doi":"10.31577/cai_2023_4_993","title":"Deep Learning Based Misogynistic Bangla Text Identification from Social Media","year":2023,"lang":"en","type":"article","venue":"Computing and Informatics","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Bengali; Hatred; Social media; Artificial intelligence; Computer science; Identification (biology); Deep learning; Confusion matrix; Hostility; Intimidation; Machine learning; Natural language processing; Psychology; World Wide Web; Social psychology; Political science","authors":[{"name":"Sonam Jahan","is_ca":false},{"name":"Raqeebir Rab","is_ca":false},{"name":"Peom Dutta","is_ca":false},{"name":"Hossain Muhammad Mahdi Hassan Khan","is_ca":false},{"name":"Muhammad Shahariar Karim Badhon","is_ca":false},{"name":"Sumaiya Binte Hassan","is_ca":true},{"name":"Ashikur Rahman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01366158206172726,"gpt":0.2320620642814693,"spread":0.2184004822197421,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003664435,0.0008743744,0.0003474859,0.001451364,0.0005430228,0.0008087414,0.0004813431,0.0007135546,0.001830869],"category_scores_gemma":[0.001371813,0.0001498716,0.0004155885,0.0008131008,0.0003003952,0.0009769351,0.0007082816,0.0006600432,0.001805927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005379622,"about_ca_system_score_gemma":0.0004241487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005269161,"about_ca_topic_score_gemma":0.01108548,"domain_scores_codex":[0.9996275,0.00007889978,0.00003412692,0.0001096775,0.00006808706,0.00008174858],"domain_scores_gemma":[0.9992448,0.0003231229,0.00009142175,0.00006322841,0.0002281714,0.00004942941],"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":[0.0009892181,0.0006721296,0.04536572,0.0005622753,0.0001559114,0.002230009,0.001560927,0.02440578,0.06542461,0.00181173,0.02808934,0.8287324],"study_design_scores_gemma":[0.00002817695,0.0003066133,0.04340177,0.0001036131,0.0001122044,0.0008484811,0.002816646,0.8934308,0.042202,0.002533102,0.01415204,0.0000645668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9033876,0.001189231,0.0732652,0.0009353628,0.0004443852,0.0002540906,0.005486326,0.002757099,0.01228082],"genre_scores_gemma":[0.9405025,0.0004266504,0.03613273,0.0001886413,0.0001219268,0.0001150505,0.008256039,0.00007667617,0.01417982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005269161,"threshold_uncertainty_score":0.01047701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2911540882","doi":"10.4149/cai_2018_6_1411","title":"Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction","year":2018,"lang":"en","type":"article","venue":"Computing and Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University of Edmonton; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Probabilistic logic; Probabilistic CTL; Computer science; Model checking; Temporal logic; Context (archaeology); Probabilistic argumentation; Linear temporal logic; Theoretical computer science; Task (project management); Probabilistic relevance model; Artificial intelligence; Property (philosophy); Machine learning; Probabilistic analysis of algorithms","authors":[{"name":"Darine Ameyed","is_ca":true},{"name":"Moeiz Miraoui","is_ca":false},{"name":"Atef Zaguia","is_ca":false},{"name":"Fehmi Jaafar","is_ca":true},{"name":"Chakib Tadj","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1038689667459356,"gpt":0.3104117797728971,"spread":0.2065428130269615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005012101,0.001445229,0.0009763332,0.002113675,0.0007854627,0.002891375,0.002174256,0.001081716,0.002230175],"category_scores_gemma":[0.01911433,0.0008473457,0.003074395,0.001267073,0.002589379,0.004998816,0.002660309,0.002892721,0.0005174773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002109375,"about_ca_system_score_gemma":0.003788696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058882,"about_ca_topic_score_gemma":0.007737676,"domain_scores_codex":[0.9933641,0.002171654,0.0005384572,0.001203349,0.002242843,0.0004796686],"domain_scores_gemma":[0.9870074,0.008633855,0.001170817,0.001858827,0.001148126,0.0001809245],"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":[0.000448442,0.0002178594,0.005029133,0.0005658228,0.0003036765,0.0008681668,0.0003459189,0.6488063,0.0125836,0.2218973,0.002539843,0.1063939],"study_design_scores_gemma":[0.00002452905,0.00003708318,0.0001421482,0.00003152432,0.00005578664,0.000095154,0.00001558998,0.93858,0.004987319,0.05452248,0.001484808,0.00002353819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005391521,0.0001277414,0.99196,0.0001770486,0.00003293185,0.00005238299,0.0001426508,0.001367572,0.0007482277],"genre_scores_gemma":[0.457461,0.0004797198,0.5386811,0.0003671813,0.0001200518,0.00033189,0.0008036692,0.000372052,0.001383379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058882,"threshold_uncertainty_score":0.02650678,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}