{"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":"83152cee9d75","filters":{"venue":"Archives for Technical Sciences"}},"results":[{"id":"W3166163918","doi":"10.7251/afts.2020.1223.029i","title":"INFLUENCE OF STRESS RATIO IN SHEAR SURFACE ON SHEAR STRENGTH OF DEPOSITED MATERIAL","year":2020,"lang":"en","type":"article","venue":"Archives for Technical Sciences","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Geotechnical engineering; Shear strength (soil); Shear stress; Shear (geology); Geology; Environmental science; Materials science; Soil water; Composite material; Soil science","authors":[{"name":"Jasmin Isabegović","is_ca":true},{"name":"Kenan Mandžić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01262810952981449,"gpt":0.2365035300946561,"spread":0.2238754205648416,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008046456,0.0000733424,0.0001519445,0.00004295843,0.00003680627,0.000007732198,0.0002629669,0.0000346138,0.00000214205],"category_scores_gemma":[0.00008867992,0.00005960953,0.00003466207,0.000237312,0.000113823,0.00005358436,0.00004080025,0.00007493899,4.583411e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003962402,"about_ca_system_score_gemma":0.00001445318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001750908,"about_ca_topic_score_gemma":0.00000733287,"domain_scores_codex":[0.9993153,0.00001623057,0.0002334042,0.0001473873,0.0001345448,0.0001531802],"domain_scores_gemma":[0.9996575,0.0001763718,0.00003303062,0.0000783224,0.000007016884,0.00004776811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003134947,0.00002406258,0.0003388489,0.00006970761,0.000002239302,4.125058e-7,0.0002102695,0.5332228,0.4637319,0.001893397,0.000004756566,0.0004702864],"study_design_scores_gemma":[0.0002029391,0.0004828938,0.003491658,0.0001342504,0.000004274686,5.860643e-7,0.00005231602,0.2231924,0.771641,0.0006747069,0.00002132807,0.0001016339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961833,0.00001856065,0.003278441,0.00007323136,0.00003434852,0.0001399414,0.00005625659,0.00007570835,0.0001401481],"genre_scores_gemma":[0.992551,0.00002336531,0.007385435,0.00001296403,0.00001340304,0.000004030167,0.000002717258,0.000005894073,0.000001226895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3100304,"threshold_uncertainty_score":0.2430806,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2956766254","doi":"10.7251/afts.2019.1120.013m","title":"PHYSICAL AND MECHANICAL SPECIFITIES OF “TENELIJA” STONE","year":2019,"lang":"en","type":"article","venue":"Archives for Technical Sciences","topic":"Regional Development and Management Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Porosity; Young's modulus; Elasticity (physics); Mechanical strength; Elastic modulus; Geology; Compressive strength; Geotechnical engineering; Materials science; Composite material","authors":[{"name":"Kenan Mandžić","is_ca":false},{"name":"Adnan Ibrahimović","is_ca":true},{"name":"Enver Mandžić","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.040753542191037,"gpt":0.2432193504676075,"spread":0.2024658082765705,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001937183,0.00006748243,0.0002291267,0.00009439707,0.00008362278,0.00001582758,0.0001983049,0.00001501382,0.00001618586],"category_scores_gemma":[0.00003850825,0.00005686901,0.00006726827,0.0001089343,0.0004700406,0.00009742232,0.0001590585,0.00003339025,0.00001731005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004075892,"about_ca_system_score_gemma":0.00000452958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006227961,"about_ca_topic_score_gemma":0.000002570699,"domain_scores_codex":[0.9993421,0.00000333277,0.0001982477,0.0002596914,0.00004174305,0.0001548733],"domain_scores_gemma":[0.9996108,0.0001932337,0.00009035278,0.00007600281,0.000004080804,0.00002554306],"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":[0.000009500986,0.00003374946,0.004381681,0.00002000879,0.00001031594,6.767223e-8,0.00007203557,0.000003549362,0.0006169394,0.9936051,0.0001779504,0.001069137],"study_design_scores_gemma":[0.000304015,0.0003283914,0.06553948,0.00001994744,0.000003549751,7.380318e-7,0.0001667007,0.001532693,0.0005747043,0.9175276,0.01384842,0.0001537212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8681861,0.0004930469,0.006511191,0.002337765,0.0001857784,0.0005123648,0.00003040561,0.00004552887,0.1216978],"genre_scores_gemma":[0.9916611,0.0002832648,0.006967879,0.00004867726,0.0000251227,0.00001954725,0.000001378358,0.000003463019,0.0009895393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.123475,"threshold_uncertainty_score":0.2319051,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7128528570","doi":"10.70102/afts.2025.1834.698","title":"OTSU AND KAPUR ENTROPY BASED OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING JAYA AND STOCHASTIC FRACTAL SEARCH ALGORITHMS FOR ENHANCED IMAGE SEGMENTATION","year":2025,"lang":"","type":"article","venue":"Archives for Technical Sciences","topic":"Medical Image Segmentation Techniques","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":"Horizon College and Seminary","funders":"","keywords":"Thresholding; Otsu's method; Image segmentation; Entropy (arrow of time); Segmentation; Pattern recognition (psychology); Image (mathematics); Image processing","authors":[{"name":"S. Anbazhagan","is_ca":false},{"name":"M. Karthika","is_ca":true},{"name":"S. Ramkumar","is_ca":false},{"name":"P. Nammalvar","is_ca":false},{"name":"P. Anbarasan","is_ca":false},{"name":"V. Krishnakumar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04195488121884374,"gpt":0.380770151292119,"spread":0.3388152700732753,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.001728066,0.0004873126,0.0005664069,0.0006591309,0.001676706,0.001234379,0.001232558,0.0001613679,0.00002157807],"category_scores_gemma":[0.00145965,0.0004490964,0.0001965682,0.0007715039,0.004790757,0.001694286,0.0009003116,0.0003966986,8.716876e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015354,"about_ca_system_score_gemma":0.0006281283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004468278,"about_ca_topic_score_gemma":0.000003147419,"domain_scores_codex":[0.9952525,0.0001973568,0.0009115331,0.001744909,0.0007831524,0.001110536],"domain_scores_gemma":[0.9951853,0.003457752,0.0003128588,0.0003986622,0.000168681,0.0004767971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000115067,0.0001749284,0.00002634034,0.0002355009,0.00002232074,0.000002431573,0.0004705194,0.0002981719,0.7446536,0.002466901,0.00005929997,0.2514749],"study_design_scores_gemma":[0.001402255,0.0007192267,0.0003033008,0.0003693229,0.00005811663,0.000007389697,0.0002483549,0.6642315,0.3265781,0.005741796,0.000007774245,0.0003329314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02374483,0.0002416758,0.9704406,0.001395267,0.0002973164,0.003450337,0.0001229865,0.0002217663,0.00008528792],"genre_scores_gemma":[0.2819324,0.00005512541,0.7171658,0.0004047227,0.00006168038,0.0003047117,0.00001286346,0.00001738018,0.0000453288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6639333,"threshold_uncertainty_score":0.9998024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7128549648","doi":"10.70102/afts.2025.1834.861","title":"METARFM: A META-LEARNING FRAMEWORK FOR THE ADAPTIVE SELECTION OF RFM MODEL ARIANTS IN CUSTOMER SEGMENTATION","year":2025,"lang":"","type":"article","venue":"Archives for Technical Sciences","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Market segmentation; Transaction data; Database transaction; Segmentation; Robustness (evolution); Scalability; Cluster analysis; Set (abstract data type); Process (computing)","authors":[{"name":"F. Mary Magdalene Jane","is_ca":false},{"name":"P. Deva Sudha","is_ca":false},{"name":"Dr.S. Saranya","is_ca":true},{"name":"Dr.P. Usha","is_ca":false},{"name":"Dr.V. Santhana Lakshmi","is_ca":false},{"name":"Dr.S.R. Kalaiselvi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07240980034480188,"gpt":0.3372231291829013,"spread":0.2648133288380994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001527848,0.0002545967,0.0004673036,0.0006807604,0.0009153657,0.0002463127,0.0005578129,0.0001074964,0.00005020135],"category_scores_gemma":[0.0007363135,0.0001768687,0.0004169828,0.002056568,0.0006877819,0.0009431102,0.0001769107,0.0003019611,0.000003026661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004277511,"about_ca_system_score_gemma":0.0001145704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001669717,"about_ca_topic_score_gemma":0.0002312023,"domain_scores_codex":[0.9978569,0.00005736888,0.0006929624,0.0005756899,0.0003578174,0.000459281],"domain_scores_gemma":[0.9970382,0.002194571,0.0005138951,0.0001395808,0.00009831772,0.00001543067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001036361,0.0004919426,0.003515855,0.0005041305,0.0008287973,2.185937e-7,0.0007283305,0.1198355,0.02081341,0.7688152,0.0003766002,0.08305375],"study_design_scores_gemma":[0.0006830608,0.0001227076,0.002857724,0.0001565612,0.001633017,2.54712e-7,0.001636665,0.7735952,0.001676229,0.2166214,0.0008101665,0.0002069631],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007467035,0.000677042,0.9816634,0.002872451,0.0003070571,0.002731578,0.0000147347,0.00005720185,0.004209475],"genre_scores_gemma":[0.9335257,0.0001579486,0.0646056,0.0004811496,0.00009252017,0.000707802,0.00001098773,0.00001302404,0.0004052295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9260587,"threshold_uncertainty_score":0.7212496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}