{"meta":{"query_hash":"7eb766131f13","filters":{"venue":"dCOBISS.SI Digital Repository"},"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/7eb766131f13","api":"https://metacan.xera.ac/api/v1/cohort?venue=dCOBISS.SI+Digital+Repository"},"results":[{"id":"W6892390062","doi":"10.5281/zenodo.10007511","title":"DEVELOPMENT OF MOULDED PULP PROTECTIVE PACKAGING FROM ALTERNATIVE FIBERS","year":2023,"lang":"en","type":"article","venue":"dCOBISS.SI Digital Repository","topic":"Natural Fiber Reinforced Composites","field":"Materials Science","cited_by":0,"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":"Cushioning; Fiber; Pulp (tooth); Cellulose fiber; Environmentally friendly; Natural fiber; Cellulose","score_opus":0.015596609440521263,"score_gpt":0.24250373950638723,"score_spread":0.22690713006586596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892390062","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":[0.9115021,0.0027403224,0.067018375,0.000059352624,0.0000940124,0.00017379315,0.00022866126,0.0004906754,0.017692683],"genre_scores_gemma":[0.8703638,0.0019437679,0.1174562,0.000022429404,0.0000085284455,0.000075514006,0.00042995534,0.00010524201,0.009594708],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998665,0.000008723712,0.0000056390413,0.000020692958,0.00007884785,0.000019618734],"domain_scores_gemma":[0.99992347,0.0000086609,0.0000149848875,0.000012159332,0.000026099537,0.0000145464455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018079109,0.00027517517,0.00014547254,0.0004694374,0.00012389365,0.00032122916,0.000260823,0.00028041488,0.0010358612],"category_scores_gemma":[0.00016993786,0.00016679522,0.0003267266,0.0002238108,0.00016689306,0.0002780625,0.00021014843,0.00029444887,0.00034515664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030837415,0.00005412269,0.00075882865,0.00027877983,0.000012586116,0.00030815674,0.00011228169,0.0052045267,0.95527554,0.0011930037,0.0001344655,0.03663678],"study_design_scores_gemma":[0.000007848383,0.0005192929,0.007060184,0.00005218907,0.000030401516,0.00052550784,0.000049558006,0.006862535,0.9560359,0.00016237672,0.02868007,0.000014016897],"about_ca_topic_score_codex":0.0006012652,"about_ca_topic_score_gemma":0.0014807697,"teacher_disagreement_score":0.0010358612,"about_ca_system_score_codex":0.00024883528,"about_ca_system_score_gemma":0.0002517099,"threshold_uncertainty_score":0.0034653544},"labels":[],"label_agreement":null},{"id":"W6893211118","doi":"10.5281/zenodo.14998760","title":"Dataset for \"Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetles\"","year":2025,"lang":"en","type":"dataset","venue":"dCOBISS.SI Digital Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Forest Service; Natural Resources Canada","funders":"Ministero dell'Università e della Ricerca; European Commission","keywords":"Agrilus; Artificial neural network; Scripting language; Pattern recognition (psychology); Convolutional neural network","score_opus":0.017733613571165128,"score_gpt":0.27124783464623164,"score_spread":0.2535142210750665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893211118","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00076379726,0.000119432414,0.0002727827,0.000072359806,0.00006952818,0.000039626164,0.9961449,0.0011672442,0.0013503279],"genre_scores_gemma":[0.0005289797,0.000034988592,0.0004835935,0.000025493879,0.0000054943894,0.00005412285,0.9981831,0.00003859003,0.0006457079],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988978,0.00011487552,0.000115373754,0.00027195353,0.0004194521,0.00018051233],"domain_scores_gemma":[0.9987452,0.0001915122,0.0001231593,0.0003355251,0.00045113557,0.0001534591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078226236,0.003785301,0.0017040004,0.0030988602,0.0008556752,0.0012184063,0.0031816352,0.0024017515,0.03987331],"category_scores_gemma":[0.0020348034,0.00047317165,0.0015680784,0.0030685645,0.0004256597,0.0009769268,0.0019162002,0.0014740904,0.06610625],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012069003,0.00007658002,0.0010269386,0.00062411034,0.000043655666,0.0000479226,0.000014675359,0.0006264539,0.0005599816,0.00020581977,0.9894215,0.0072317296],"study_design_scores_gemma":[0.0004535117,0.0001452647,0.015902294,0.000491392,0.00010050174,0.00033158934,0.00020694788,0.0048962706,0.0037324622,0.0013818049,0.9722403,0.000117675845],"about_ca_topic_score_codex":0.024712607,"about_ca_topic_score_gemma":0.06294806,"teacher_disagreement_score":0.03987331,"about_ca_system_score_codex":0.0013912088,"about_ca_system_score_gemma":0.001673127,"threshold_uncertainty_score":0.13338947},"labels":[],"label_agreement":null},{"id":"W7082363540","doi":"10.1103/1c4s-hw47","title":"Detecting dark matter subhalos in the Galactic plane with the Cherenkov Telescope Array Observatory","year":2025,"lang":"en","type":"article","venue":"dCOBISS.SI Digital Repository","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Perimeter Institute","funders":"Agence Nationale de la Recherche","keywords":"Dark matter; Observatory; Telescope; Galactic plane; Cherenkov radiation; Cherenkov Telescope Array","score_opus":0.008859670025464743,"score_gpt":0.19722633019562638,"score_spread":0.18836666017016163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7082363540","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":[0.5881001,0.0074082036,0.055585675,0.004516853,0.00034678465,0.00034867603,0.22945191,0.013694677,0.10054714],"genre_scores_gemma":[0.77834994,0.0027806829,0.07926961,0.0006954254,0.00023906799,0.00014791233,0.11936964,0.0014758134,0.017671827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999762,0.000026611275,0.000011378783,0.000063684194,0.00008531296,0.00005107852],"domain_scores_gemma":[0.99963284,0.00003713248,0.00006458467,0.000100330966,0.00006202363,0.00010308638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048838404,0.0002883501,0.0003873077,0.0018165559,0.00035828154,0.0009565368,0.00042687816,0.00031671338,0.008209837],"category_scores_gemma":[0.00057567767,0.00028603806,0.00022004881,0.0021866632,0.00015603323,0.00061107497,0.0014698334,0.000389233,0.00454987],"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":[0.0010169238,0.00025759585,0.3468203,0.00052333495,0.00040016,0.00066405424,0.0006038843,0.003284062,0.1035204,0.006138438,0.16886713,0.36790386],"study_design_scores_gemma":[0.00020850924,0.0001233648,0.81797206,0.00021168857,0.00016051534,0.00041076646,0.0003628522,0.010543386,0.020633088,0.0061063664,0.14318538,0.000082106686],"about_ca_topic_score_codex":0.009009987,"about_ca_topic_score_gemma":0.024880577,"teacher_disagreement_score":0.009009987,"about_ca_system_score_codex":0.00027367155,"about_ca_system_score_gemma":0.00055409636,"threshold_uncertainty_score":0.027464628},"labels":[],"label_agreement":null}]}