{"meta":{"query_hash":"1c9e18d414ee","filters":{"venue":"Baltic Journal of Modern Computing"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/1c9e18d414ee","api":"https://metacan.xera.ac/api/v1/cohort?venue=Baltic+Journal+of+Modern+Computing"},"results":[{"id":"W3136121419","doi":"10.22364/bjmc.2021.9.1.03","title":"High F-score Model for Recognizing Object Visibility in Images with Occluded Objects of Interest","year":2021,"lang":"en","type":"article","venue":"Baltic Journal of Modern Computing","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Visibility; Artificial intelligence; Computer vision; Object (grammar); Computer science; Computer graphics (images); Geography","score_opus":0.08044920229076,"score_gpt":0.3087923481758358,"score_spread":0.2283431458850758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136121419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60542375,0.0075645954,0.36206937,0.0016102544,0.0009771686,0.00047180374,0.0027874317,0.0064638625,0.012631753],"genre_scores_gemma":[0.9463014,0.0006671093,0.0423488,0.00026827224,0.00026478266,0.00011660936,0.0030166258,0.0001372323,0.006879264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877614,0.00014202157,0.000060539143,0.00043037045,0.00036343897,0.00022758725],"domain_scores_gemma":[0.99849117,0.00053290447,0.000117492775,0.00011709601,0.00064050494,0.00010078954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001911107,0.0013672593,0.0015067726,0.0026742953,0.000681907,0.0015089378,0.0012332997,0.001792568,0.002749142],"category_scores_gemma":[0.0035414011,0.00020446186,0.0015424518,0.0009041758,0.00047318832,0.0013363474,0.000747407,0.0012849077,0.0020476698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002206154,0.0013161411,0.038755655,0.00042403545,0.0006320779,0.0006540677,0.00024526689,0.052134976,0.048795294,0.0011653976,0.025335688,0.8283354],"study_design_scores_gemma":[0.000062905616,0.0008718639,0.05160204,0.00007732625,0.00024075585,0.0006985136,0.00015686585,0.92444396,0.016503483,0.0016620457,0.0036199272,0.00006036342],"about_ca_topic_score_codex":0.01536488,"about_ca_topic_score_gemma":0.012032374,"teacher_disagreement_score":0.01536488,"about_ca_system_score_codex":0.00147254,"about_ca_system_score_gemma":0.00084213866,"threshold_uncertainty_score":0.030550897},"labels":[],"label_agreement":null},{"id":"W4237419897","doi":"10.22364/bjmc.2021.9.1.3","title":"High F-score Model for Recognizing Object Visibility in Images with Occluded Objects of Interest","year":2021,"lang":"en","type":"article","venue":"Baltic Journal of Modern Computing","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Visibility; Artificial intelligence; Computer vision; Object (grammar); Computer science; Region of interest; Computer graphics (images); Geography","score_opus":0.08044920229076,"score_gpt":0.3087923481758358,"score_spread":0.2283431458850758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237419897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60542375,0.0075645954,0.36206937,0.0016102544,0.0009771686,0.00047180374,0.0027874317,0.0064638625,0.012631753],"genre_scores_gemma":[0.9463014,0.0006671093,0.0423488,0.00026827224,0.00026478266,0.00011660936,0.0030166258,0.0001372323,0.006879264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877614,0.00014202157,0.000060539143,0.00043037045,0.00036343897,0.00022758725],"domain_scores_gemma":[0.99849117,0.00053290447,0.000117492775,0.00011709601,0.00064050494,0.00010078954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001911107,0.0013672593,0.0015067726,0.0026742953,0.000681907,0.0015089378,0.0012332997,0.001792568,0.002749142],"category_scores_gemma":[0.0035414011,0.00020446186,0.0015424518,0.0009041758,0.00047318832,0.0013363474,0.000747407,0.0012849077,0.0020476698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002206154,0.0013161411,0.038755655,0.00042403545,0.0006320779,0.0006540677,0.00024526689,0.052134976,0.048795294,0.0011653976,0.025335688,0.8283354],"study_design_scores_gemma":[0.000062905616,0.0008718639,0.05160204,0.00007732625,0.00024075585,0.0006985136,0.00015686585,0.92444396,0.016503483,0.0016620457,0.0036199272,0.00006036342],"about_ca_topic_score_codex":0.01536488,"about_ca_topic_score_gemma":0.012032374,"teacher_disagreement_score":0.01536488,"about_ca_system_score_codex":0.00147254,"about_ca_system_score_gemma":0.00084213866,"threshold_uncertainty_score":0.030550897},"labels":[],"label_agreement":null},{"id":"W4408967777","doi":"10.22364/bjmc.2025.13.1.13","title":"Applying Word Embeddings for Lithuanian Morphology: The Case of Adjectival Participles","year":2025,"lang":"en","type":"article","venue":"Baltic Journal of Modern Computing","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; Atomic Energy of Canada Limited; University of Galway","keywords":"Lithuanian; Morphology (biology); Linguistics; Word (group theory); Word formation; Computer science; Natural language processing; Artificial intelligence; Philosophy; Biology; Zoology","score_opus":0.022118085991670047,"score_gpt":0.32303248215102787,"score_spread":0.30091439615935783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408967777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57460815,0.0015686813,0.40414533,0.0008725583,0.00031727226,0.00022180067,0.0011088876,0.0014989432,0.01565833],"genre_scores_gemma":[0.83858734,0.0006399454,0.15578422,0.00007013765,0.000038517974,0.00008787698,0.00082073547,0.00024169892,0.0037296268],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998963,0.00042815617,0.0001281207,0.00024236848,0.00017306698,0.00006527441],"domain_scores_gemma":[0.99739504,0.0013671225,0.00023863,0.00044991644,0.0005035722,0.00004578948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010293247,0.000674179,0.00032091726,0.0019788023,0.00073916855,0.0022603816,0.00032267423,0.0005632038,0.0032389513],"category_scores_gemma":[0.005231697,0.0002740298,0.0005711428,0.0021701553,0.00089627673,0.0036608237,0.001882271,0.0007511159,0.0013036764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004264529,0.00013928577,0.02796982,0.0010509235,0.00016301915,0.0022064678,0.0152868675,0.009627309,0.04953998,0.026924247,0.0037474586,0.8629182],"study_design_scores_gemma":[0.00008734588,0.00095567346,0.08655397,0.0011033933,0.0004895914,0.011005884,0.04756755,0.35869563,0.15160124,0.14726569,0.19426033,0.00041373403],"about_ca_topic_score_codex":0.0014687624,"about_ca_topic_score_gemma":0.0021376915,"teacher_disagreement_score":0.0032389513,"about_ca_system_score_codex":0.0003447033,"about_ca_system_score_gemma":0.0006562271,"threshold_uncertainty_score":0.01083535},"labels":[],"label_agreement":null}]}