{"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":"8e6f6f877fd4","filters":{"venue":"Journal of South Asian Logistics and Transport"}},"results":[{"id":"W4387104324","doi":"10.4038/jsalt.v3i2.68","title":"Investigation of automation opportunities in warehouse management in construction supply chains using convolutional neural networks","year":2023,"lang":"en","type":"article","venue":"Journal of South Asian Logistics and Transport","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Transport Canada","funders":"","keywords":"Convolutional neural network; Automation; Supply chain; Computer science; Inventory management; Object (grammar); Artificial intelligence; Warehouse; Deep learning; Supply chain management; Artificial neural network; Data science; Knowledge management; Operations research; Operations management; Business; Marketing; Engineering","authors":[{"name":"A. Dissanayake","is_ca":true},{"name":"P. T. R. S. Sugathadasa","is_ca":false},{"name":"M. Mavin De Silva","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03626936672976816,"gpt":0.2199289781428862,"spread":0.183659611413118,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001893141,0.00007759905,0.0001546968,0.0003426852,0.00001918174,0.00000679097,0.00003671922,0.00005316636,0.000001819457],"category_scores_gemma":[0.000004053127,0.0000767618,0.00002747507,0.0001787359,0.00007336773,0.000099258,0.000003568637,0.0001457803,4.829458e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004726229,"about_ca_system_score_gemma":0.00001900998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008799188,"about_ca_topic_score_gemma":0.00001031893,"domain_scores_codex":[0.9992774,0.00000978655,0.000417079,0.00005009984,0.0001167008,0.0001289],"domain_scores_gemma":[0.9997672,0.000009063368,0.0001077217,0.00003575794,0.00004151572,0.00003872904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001718735,0.000002203814,0.2177269,0.0001132321,0.00002192716,0.0001326805,0.001218101,0.776397,0.00005986489,0.001787246,0.000004715941,0.002518942],"study_design_scores_gemma":[0.0005015801,0.00002232064,0.6136614,0.0001706884,0.00002636472,0.00003375198,0.002661539,0.3820686,0.00003885579,0.0007323436,0.000006621629,0.00007591812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593602,0.0000499632,0.03998871,0.00002788859,0.0003924564,0.00006633352,0.00001099795,0.00001961985,0.0000837783],"genre_scores_gemma":[0.9977062,0.0001413142,0.002037612,0.000005085781,0.00008414955,9.962051e-7,0.00001295951,0.00000931362,0.000002402203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3959345,"threshold_uncertainty_score":0.3130255,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393276688","doi":"10.4038/jsalt.v4i1.90","title":"An Agent-Based Crowd Dynamics Simulation that Considers Idling and Time-and-Distance-Conscious Optimising Behaviour","year":2024,"lang":"en","type":"article","venue":"Journal of South Asian Logistics and Transport","topic":"Evacuation and Crowd Dynamics","field":"Engineering","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 Toronto; University of Calgary","funders":"","keywords":"Dynamics (music); Crowd simulation; Computer science; Simulation; Psychology; Crowds; Computer security","authors":[{"name":"Asiri P. Senasinghe","is_ca":true},{"name":"Willem Klumpenhouwer","is_ca":true},{"name":"Ahmed Labidi","is_ca":true},{"name":"Lina Kattan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01598610169360965,"gpt":0.2476558594061665,"spread":0.2316697577125568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002309427,0.000149114,0.000206111,0.000142943,0.0000763893,0.0001390929,0.00004214836,0.00009667948,0.00001054109],"category_scores_gemma":[0.0000102899,0.0001424913,0.00004966881,0.00006764154,0.0001019984,0.0001481632,0.000002277087,0.0002346624,5.811783e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004253683,"about_ca_system_score_gemma":0.00004584108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000024832,"about_ca_topic_score_gemma":0.00002179595,"domain_scores_codex":[0.9992365,0.0000115883,0.0003284642,0.0001250588,0.0001554821,0.0001429033],"domain_scores_gemma":[0.9995877,0.00005452388,0.00006725409,0.00007117273,0.00005663661,0.000162744],"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":[0.00003582159,0.00001989843,0.03096194,0.000215484,0.00007750628,0.0004008889,0.00165298,0.9598805,0.0001076066,0.001056854,0.000004634891,0.005585891],"study_design_scores_gemma":[0.0004007648,0.00005995856,0.009547938,0.0001406278,0.0002035372,0.00006525127,0.0005843754,0.9883802,0.00001362635,0.0004034279,0.00003626888,0.0001640284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3521405,0.0008048716,0.6463652,0.00008775239,0.0001644517,0.00006731416,0.0001208609,0.00006253771,0.00018642],"genre_scores_gemma":[0.9945824,0.00008732374,0.005177422,0.00003100254,0.00004338268,4.171581e-7,0.00003490919,0.00002997484,0.00001312607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6424419,"threshold_uncertainty_score":0.5810626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}