{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":7,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":7,"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":"d029c48f14fe","filters":{"venue":"4TU.ResearchData"}},"results":[{"id":"W6947825955","doi":"10.4121/14573079","title":"Spatial distribution of maximum canopy height and heights percentiles in Canada at 250m spatial resolution","year":2021,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Percentile; Spatial distribution; Lidar; Canopy; Normalized Difference Vegetation Index; Regression analysis; Tree canopy","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.02492126357780294,"gpt":0.2744034951010184,"spread":0.2494822315232155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001388693,0.0008273397,0.001280592,0.0005795386,0.0003999991,0.000169663,0.001687397,0.0005774408,0.002358902],"category_scores_gemma":[0.001327173,0.0008502959,0.0001264683,0.0009634653,0.0006577494,0.0003897583,0.003974863,0.001639831,0.0002818562],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.008050577,"about_ca_system_score_gemma":0.009125524,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959933,"about_ca_topic_score_gemma":0.9995649,"domain_scores_codex":[0.9902936,0.00146039,0.001274453,0.001795075,0.003382845,0.001793597],"domain_scores_gemma":[0.9950069,0.0003895654,0.0005400541,0.002833355,0.0005354406,0.0006947494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008103032,0.0002236059,0.002350491,0.0007277752,0.0001315038,0.0009649417,0.00002454158,0.0000148206,0.000449839,0.000008161572,0.9936168,0.0006771955],"study_design_scores_gemma":[0.001690886,0.0001102083,0.04686397,0.0006581535,0.0001177494,0.0000693102,0.000101551,0.00106628,0.0008476024,0.00004603418,0.9476106,0.0008176267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03577185,0.001601148,0.00002669444,0.000240783,0.0005735211,0.001078672,0.9606553,0.00002466118,0.00002735695],"genre_scores_gemma":[0.04821804,0.001736531,0.00002364436,0.00001878041,0.0004144533,0.00009108004,0.9492732,0.0001126362,0.0001116156],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04600619,"threshold_uncertainty_score":0.9993948,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6891674473","doi":"10.4121/a09bedfa-ad7b-4835-b7f4-6e6cde811611.v7","title":"ERIES: Engineering Research Infrastructures for European Synergies","year":2025,"lang":"en","type":"other","venue":"4TU.ResearchData","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Frontier; Testbed; Sustainable development; Key (lock); Resilience (materials science); Interoperability; Engineering research; Best practice","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.08367816995556696,"gpt":0.4067093630218578,"spread":0.3230311930662908,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005706113,0.0008433923,0.0008782347,0.004662362,0.0004110242,0.0007970514,0.005078223,0.0004864111,0.007497466],"category_scores_gemma":[0.01141588,0.0008217189,0.0002371072,0.002016043,0.0008656238,0.0002631253,0.004613797,0.002431907,0.006753048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004220183,"about_ca_system_score_gemma":0.001075062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001564255,"about_ca_topic_score_gemma":0.0006419964,"domain_scores_codex":[0.991425,0.001088558,0.0006034797,0.001730545,0.0025058,0.002646579],"domain_scores_gemma":[0.9931237,0.001667139,0.0001669137,0.003881628,0.0006673475,0.0004933503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001388144,0.00004664697,0.00002338865,0.001669062,0.000434312,0.00008986875,0.0001167118,0.00005687424,0.00137987,0.008666264,0.9835188,0.003859389],"study_design_scores_gemma":[0.0007594048,0.0001153398,0.0002440267,0.001401835,0.00003888487,0.000005255718,0.000188357,0.0003435079,0.0004902227,0.0006529596,0.9950655,0.000694695],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002445411,0.003835086,0.001456631,0.0003249522,0.0007388146,0.003357158,0.0306796,0.001967228,0.9576161],"genre_scores_gemma":[0.0007868661,0.0006891341,0.008489029,0.00003717787,0.00239016,0.0005399814,0.004825479,0.004701524,0.9775407],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02585412,"threshold_uncertainty_score":0.9998695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6928966517","doi":"10.4121/20067578","title":"Data from questionnaire on the acceptance of plant-based meat alternatives.","year":2022,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Data collection; Meat packing industry; Willingness to pay; Statistical analysis; Work (physics)","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.08844874805249243,"gpt":0.3317438572373334,"spread":0.2432951091848409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008129675,0.0003146265,0.0003146089,0.00009794787,0.0002206568,0.00009339694,0.005725397,0.0001932766,0.004662402],"category_scores_gemma":[0.0008909238,0.0002256635,0.00009154085,0.0001494881,0.0001688232,0.00001658303,0.003935983,0.0007006737,0.00004513562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002682311,"about_ca_system_score_gemma":0.000362106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009786854,"about_ca_topic_score_gemma":0.0005432736,"domain_scores_codex":[0.9975401,0.0003010626,0.0004285274,0.0006362944,0.0007354134,0.0003586732],"domain_scores_gemma":[0.9941633,0.000145737,0.0003139862,0.005215067,0.00008045341,0.00008140173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002495345,0.0001411591,0.00003045975,0.00009888369,0.0002049026,0.000005544251,0.000008124717,0.00003153224,0.0005965603,0.00002472667,0.9982542,0.0003543928],"study_design_scores_gemma":[0.000442615,0.0002351964,0.0002075879,0.00009938407,0.00003919053,0.000002395707,0.0000951088,0.0007141216,0.002858067,0.00002658047,0.9950309,0.0002488031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001029654,0.001435482,0.00006925072,0.0005293349,0.0002532702,0.0004714466,0.9961105,0.000006590304,0.00009445177],"genre_scores_gemma":[0.001561373,0.001887105,0.00007770691,0.0003432719,0.0002876949,0.00006886049,0.9955993,0.00002961804,0.0001451012],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004617266,"threshold_uncertainty_score":0.9996541,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6966734023","doi":"10.4121/20067581","title":"Data collected on the acceptance of plant-based foods in Canada","year":2022,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Willingness to pay; Data collection; Statistical analysis; Population","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.1858705606275131,"gpt":0.3599313683817309,"spread":0.1740608077542178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.004704391,0.0007141765,0.001056291,0.001105243,0.0004372016,0.0001503451,0.02283502,0.0002144295,0.085832],"category_scores_gemma":[0.007866759,0.0005866428,0.00007227785,0.004526607,0.0004330121,0.0002644463,0.009201852,0.003988361,0.0003982721],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00363283,"about_ca_system_score_gemma":0.03332036,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9749302,"about_ca_topic_score_gemma":0.9987008,"domain_scores_codex":[0.9870669,0.002960201,0.001066134,0.001860329,0.005379549,0.001666924],"domain_scores_gemma":[0.9785267,0.004969977,0.0005862727,0.01540231,0.0001823059,0.0003324864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001182383,0.0003337972,0.0001423682,0.0002773052,0.0001741635,0.0006261746,0.000007409136,0.0001148448,0.00003815618,0.00001487878,0.9970049,0.00008358862],"study_design_scores_gemma":[0.00122341,0.0001692828,0.00106625,0.0002262561,0.00004767604,0.000006590322,0.0002003535,0.001370873,0.00009827667,0.000009613201,0.9950358,0.0005456638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004285843,0.0002808506,4.251373e-7,0.0007657065,0.0003382281,0.002197914,0.9957688,0.00003390019,0.0001856345],"genre_scores_gemma":[0.0005863912,0.0001856489,0.00002201295,0.0004367828,0.00009164967,0.0004209421,0.9979188,0.0001778154,0.0001599871],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08543373,"threshold_uncertainty_score":0.9996585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6966338467","doi":"10.4121/21127528.v1","title":"Wetland Classification with Deep ResU-Net Convolutional Neural Network and Multitemporal Sentinel-1 & 2 Imagery and ALOS Elevation Data: A Case Study in Alberta Parkland & Grassland Natural Region, Canada","year":2022,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Support vector machine; Elevation (ballistics); Wetland; Random forest; Convolutional neural network; Satellite; Satellite imagery; Grassland","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.09349743064957579,"gpt":0.3367917454669007,"spread":0.2432943148173249,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003511803,0.001050136,0.001113132,0.0008950389,0.001165501,0.0008172372,0.001740566,0.0002920753,0.0003813641],"category_scores_gemma":[0.001975381,0.0009648643,0.00004141773,0.001786304,0.0007460564,0.001553199,0.003624927,0.003092093,0.00002595437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007831301,"about_ca_system_score_gemma":0.002076879,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9115113,"about_ca_topic_score_gemma":0.9979421,"domain_scores_codex":[0.9887485,0.002727175,0.001310909,0.002980961,0.002611001,0.001621443],"domain_scores_gemma":[0.991745,0.00214454,0.0008500088,0.004255638,0.0004400411,0.0005647816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001515483,0.0003164435,0.261274,0.0002001844,0.0002654725,0.004567318,0.00008825448,0.00009129313,0.000007688305,0.000001800166,0.7316253,0.00004684023],"study_design_scores_gemma":[0.005774577,0.0001280846,0.1569474,0.0001249146,0.0002949213,0.006676208,0.0006288972,0.009587544,6.629703e-8,0.00001346259,0.8186276,0.001196351],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.261741,0.0009735259,0.000006886513,0.0005641204,0.0002540249,0.002748098,0.733667,0.00003962962,0.000005652213],"genre_scores_gemma":[0.118236,0.0002539782,0.00009620239,0.00009348921,0.0004661889,0.0003809218,0.8799058,0.0001548708,0.0004125614],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1462388,"threshold_uncertainty_score":0.9992802,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6947739232","doi":"10.4121/16686154.v2","title":"Soil organic carbon stock and uncertainties, 30cm and 1m depth, at 250m spatial resolution in Canada, version 3.0","year":2021,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Soil carbon; Permafrost; Soil water; Land cover; Stock (firearms); Spatial variability; Hydrology (agriculture); Quantile; Ground truth","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01352620630713846,"gpt":0.2233906860049172,"spread":0.2098644796977788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004575595,0.0003540223,0.0003786633,0.00009272309,0.0002589761,0.0001046026,0.0006006564,0.0003262995,0.00080321],"category_scores_gemma":[0.0004427813,0.0003121682,0.00002451093,0.000446924,0.0003284541,0.0001385987,0.003661572,0.0009453354,0.00005557504],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004027121,"about_ca_system_score_gemma":0.0005848141,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9822751,"about_ca_topic_score_gemma":0.9983926,"domain_scores_codex":[0.9962603,0.0003747107,0.0003049385,0.001025185,0.00130594,0.000728904],"domain_scores_gemma":[0.9983755,0.0001661302,0.0001172719,0.0009974836,0.00002524207,0.0003183913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005311283,0.00002157016,0.003474521,0.00007646771,0.00001467897,0.00030118,0.00003396371,0.00009666738,0.001116824,8.387723e-8,0.9933473,0.001463611],"study_design_scores_gemma":[0.0007030278,0.0000843626,0.08396256,0.0002414501,0.00004270185,0.0001196486,0.0002667507,0.004325044,0.000315762,0.00001291156,0.9093132,0.000612586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3140502,0.000887094,0.00000155657,0.0007408728,0.00039418,0.0005836947,0.6829726,0.00001631308,0.0003535475],"genre_scores_gemma":[0.02147261,0.002545587,0.00007929425,0.0001233567,0.0001687995,0.000004157461,0.9745094,0.00003532467,0.001061515],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2925776,"threshold_uncertainty_score":0.9999331,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W6947967946","doi":"10.4121/16686154","title":"Soil organic carbon stock and uncertainties, 30cm and 1m depth, at 250m spatial resolution in Canada, version 3.0","year":2022,"lang":"en","type":"dataset","venue":"4TU.ResearchData","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Permafrost; Soil carbon; Soil water; Land cover; Stock (firearms); Spatial variability; Hydrology (agriculture); Digital soil mapping","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01191999921271184,"gpt":0.2626321928691009,"spread":0.2507121936563891,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005633219,0.0002940102,0.0003364675,0.0002345556,0.0001942862,0.00005239688,0.0006472539,0.0002534214,0.001158566],"category_scores_gemma":[0.0003627956,0.0003203152,0.00003938931,0.0002253079,0.0001618002,0.000007883666,0.003910285,0.0005770238,0.000001951846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007871423,"about_ca_system_score_gemma":0.001349934,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9308807,"about_ca_topic_score_gemma":0.9918218,"domain_scores_codex":[0.9973612,0.0003303242,0.0002987921,0.0009060229,0.0005975953,0.0005060014],"domain_scores_gemma":[0.9983356,0.00004719222,0.0001235043,0.001248072,0.00007312076,0.0001724931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001778025,0.00002798255,0.001022238,0.00009679861,0.00006077188,0.0000951678,0.000006126797,0.000004751793,0.01538952,1.223253e-7,0.9823533,0.0007654662],"study_design_scores_gemma":[0.0004978022,0.0002177061,0.001036316,0.00002983239,0.0000684893,0.00002555109,0.00008097821,0.0004378506,0.0098183,0.000006678514,0.9873919,0.000388583],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1918945,0.004550749,0.00001095921,0.0002204595,0.00007731267,0.0006259346,0.802465,0.00001468336,0.0001404013],"genre_scores_gemma":[0.02894365,0.007970249,0.000018438,0.00009705585,0.000105837,0.0000494396,0.9621989,0.00003502033,0.0005813742],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1629509,"threshold_uncertainty_score":0.9999249,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}