{"meta":{"query_hash":"cdb9f1179c96","filters":{"venue":"The Plant Phenome Journal"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"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/cdb9f1179c96","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+Plant+Phenome+Journal"},"results":[{"id":"W3012339065","doi":"10.1002/ppj2.20000","title":"Plot extraction from aerial imagery: A precision agriculture approach","year":2020,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Plot (graphics); Aerial imagery; Computer science; Pipeline (software); Aerial image; Computer vision; Workflow; Satellite imagery; Artificial intelligence; Overlay; Remote sensing; Geographic information system; Photogrammetry; Georeference; Image (mathematics); Geography; Mathematics; Database; Statistics","score_opus":0.02231298261651207,"score_gpt":0.21132694047766584,"score_spread":0.18901395786115377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012339065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93413323,0.00010299399,0.029517973,0.0054405886,0.00032892148,0.0002855886,0.00008434806,0.00008484194,0.030021537],"genre_scores_gemma":[0.99208426,0.000055718738,0.005753798,0.00041610035,0.0014572798,0.0000012484514,0.000045357694,0.00001132586,0.00017492769],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991162,0.00008526829,0.00017760938,0.00017164949,0.0002933434,0.00015597274],"domain_scores_gemma":[0.9995276,0.000060415954,0.00013263652,0.00013068215,0.000005647317,0.00014302737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017356852,0.00010081558,0.00010483867,0.0000082314755,0.0003594888,0.00013189229,0.0002553642,0.00004356256,0.0004400015],"category_scores_gemma":[0.000025086898,0.00005617225,0.00005892462,0.00013451818,0.000051375166,0.0001492195,0.000067002926,0.0003838949,0.00047312275],"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":[0.00027391734,0.00016906341,0.00046622206,0.0000023852733,0.0000535471,0.000014368445,0.006547174,0.008586958,0.84075934,0.0000452061,0.12358054,0.019501261],"study_design_scores_gemma":[0.0027541071,0.0003814932,0.18313156,0.00006631659,0.0003995038,0.002935461,0.004542584,0.07485763,0.0155171715,0.0072361673,0.70684254,0.0013354484],"about_ca_topic_score_codex":0.00011151831,"about_ca_topic_score_gemma":0.000004968091,"teacher_disagreement_score":0.82524216,"about_ca_system_score_codex":0.000057182933,"about_ca_system_score_gemma":0.000008031247,"threshold_uncertainty_score":0.60811937},"labels":[],"label_agreement":null},{"id":"W3185881337","doi":"10.1002/ppj2.20019","title":"Measuring canopy height in soybean and wheat using a low‐cost depth camera","year":2021,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Canopy; Phenomics; Growing season; Point cloud; Remote sensing; Environmental science; Throughput; Agronomy; Mathematics; Geography; Computer science; Biology; Artificial intelligence; Botany","score_opus":0.03906246112049565,"score_gpt":0.2265005144279661,"score_spread":0.18743805330747043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185881337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925978,0.000198753,0.00078222016,0.0006459953,0.00006938961,0.00007023115,0.0000054410766,0.000007352981,0.005622816],"genre_scores_gemma":[0.99826,0.00009475273,0.0012321131,0.00016479506,0.00010141231,3.9054106e-7,0.0000022808038,0.000009522476,0.00013471198],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99917233,0.00008791598,0.00017203728,0.00014254921,0.00019168337,0.00023348606],"domain_scores_gemma":[0.99963164,0.000044200628,0.00006123653,0.00015046529,0.0000063234634,0.00010615253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000317297,0.00008811786,0.000109332526,0.000027573566,0.0003332198,0.00010205436,0.00011559272,0.000024891937,0.0001228954],"category_scores_gemma":[0.000014383554,0.00006299826,0.000026225636,0.00015821676,0.00007366369,0.00009183733,0.000077737204,0.00028539682,0.000026538923],"study_design_candidate":"observational","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":[0.000099097844,0.00031113508,0.19525774,0.000027004144,0.00010183672,0.0006715969,0.015710961,0.025022168,0.71159035,0.0002436587,0.0014900316,0.04947442],"study_design_scores_gemma":[0.0032453288,0.00006612183,0.76986635,0.0006927324,0.00020955174,0.032415953,0.0062796767,0.118456766,0.036005992,0.005509169,0.025708137,0.0015442261],"about_ca_topic_score_codex":0.00039360303,"about_ca_topic_score_gemma":0.0010884351,"teacher_disagreement_score":0.6755844,"about_ca_system_score_codex":0.00016318413,"about_ca_system_score_gemma":0.000036254958,"threshold_uncertainty_score":0.25689945},"labels":[],"label_agreement":null},{"id":"W3197434167","doi":"10.1002/ppj2.20023","title":"Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants","year":2021,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Latent variable; Variance (accounting); Selection (genetic algorithm); Set (abstract data type); Latent variable model; Variation (astronomy); Artificial intelligence; Computer science; Machine learning","score_opus":0.025608061753063117,"score_gpt":0.23582157307108273,"score_spread":0.2102135113180196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197434167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917321,0.00070401386,0.0000037163588,0.005253885,0.00010481829,0.00013646537,0.00020637634,0.0000049677474,0.0018536102],"genre_scores_gemma":[0.9992336,0.00032754143,0.0000061681035,0.0002239069,0.000025945907,0.0000037648736,0.0000093594135,0.0000045720867,0.00016515447],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989282,0.0002232236,0.00024451697,0.0001264119,0.00025878398,0.00021885532],"domain_scores_gemma":[0.9994243,0.0001404399,0.00013128214,0.00023995107,0.000011097305,0.000052947093],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010547932,0.00009280266,0.00012566175,0.000008525701,0.00032593397,0.000063111635,0.00028603268,0.000027381411,0.004049656],"category_scores_gemma":[0.00009758132,0.00004141256,0.00004364105,0.00013720238,0.000284826,0.000076916374,0.00021475127,0.0002863907,0.000018734634],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002356225,0.0009323222,0.7557321,0.000080474536,0.0002894479,0.0003960877,0.075500235,0.0008502415,0.11320628,0.0037165582,0.029090516,0.017849522],"study_design_scores_gemma":[0.00039956218,0.00003329147,0.9868722,0.00002618098,0.0000145452395,0.00040798858,0.0070842905,0.00017110075,0.0011029477,0.00094113644,0.0028780808,0.000068703426],"about_ca_topic_score_codex":0.00007862744,"about_ca_topic_score_gemma":0.0005310367,"teacher_disagreement_score":0.23114008,"about_ca_system_score_codex":0.00014666632,"about_ca_system_score_gemma":0.000023912877,"threshold_uncertainty_score":0.9968608},"labels":[],"label_agreement":null},{"id":"W4226051497","doi":"10.1002/ppj2.20037","title":"Genotypic variability in root length in pea (<i>Pisum sativum</i> L.) and lentil (<i>Lens culinaris</i> Medik.) cultivars in a semi‐arid environment based on mini‐rhizotron image capture","year":2022,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"USA Dry Pea and Lentil Council; Montana State University","keywords":"Sativum; Biology; Pisum; Cultivar; Field pea; Agronomy; Arid; Root system; Yield (engineering); Lateral root; Horticulture; Ecology","score_opus":0.011826955647305792,"score_gpt":0.17458208272716655,"score_spread":0.16275512707986076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226051497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945261,0.0005156463,0.000005992036,0.003788554,0.00009074985,0.00029342555,0.00024305863,0.0000067811948,0.0005297115],"genre_scores_gemma":[0.99900633,0.00043063881,0.00006317555,0.0003029552,0.00007735661,0.000037728863,0.000031425396,0.0000026942369,0.000047715755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977072,0.0006638851,0.00041172784,0.00035748997,0.00041720955,0.0004425039],"domain_scores_gemma":[0.9993477,0.00033262183,0.00014313735,0.00009236677,0.0000036383506,0.00008051921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015239416,0.00023430766,0.00029979667,0.000038554557,0.00039005946,0.000037425478,0.0003065915,0.000056307657,0.00047578642],"category_scores_gemma":[0.000029296372,0.00009878031,0.000065025226,0.0001746297,0.00014111013,0.000071521674,0.0002785886,0.00083029567,0.000007324094],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001230209,0.0021440447,0.8335305,0.000029710825,0.000044355653,0.00024344529,0.0062566777,0.016886322,0.13264583,0.000048905044,0.0007292173,0.006210778],"study_design_scores_gemma":[0.0007883264,0.00033675722,0.9906954,0.000025189158,0.000015552583,0.000057503537,0.0034670879,0.0009848229,0.00011061049,0.0004386329,0.0028496124,0.0002305025],"about_ca_topic_score_codex":0.00048684646,"about_ca_topic_score_gemma":0.0012458874,"teacher_disagreement_score":0.1571649,"about_ca_system_score_codex":0.00034112588,"about_ca_system_score_gemma":0.000013283478,"threshold_uncertainty_score":0.52095276},"labels":[],"label_agreement":null},{"id":"W4226337120","doi":"10.1002/ppj2.20039","title":"A semi‐automatic workflow for plot boundary extraction of irregularly sized and spaced field plots from UAV imagery","year":2022,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Normalized Difference Vegetation Index; Plot (graphics); Artificial intelligence; Pixel; Computer science; Remote sensing; Filter (signal processing); Thresholding; Computer vision; Pattern recognition (psychology); Mathematics; Leaf area index; Image (mathematics); Statistics; Geography","score_opus":0.01042723552801677,"score_gpt":0.2062767451141448,"score_spread":0.195849509586128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226337120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952285,0.00027597227,0.0013730883,0.0019886177,0.0004030016,0.00026062533,0.0000698687,0.00002782447,0.0003724964],"genre_scores_gemma":[0.9919677,0.000052689553,0.0069865026,0.00030156638,0.00021739822,0.000004548413,0.00001936447,0.00001810618,0.00043215914],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988119,0.00015030973,0.000270869,0.00015891017,0.00039631093,0.00021168013],"domain_scores_gemma":[0.9988809,0.00057141436,0.00029956922,0.00016599921,0.000008092675,0.00007402973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049679115,0.00012602346,0.00019945233,0.000028859726,0.0005773479,0.00010125413,0.00022752486,0.000037842972,0.0006682424],"category_scores_gemma":[0.00006972315,0.00008203314,0.000079125726,0.00011897438,0.000081909704,0.00018789747,0.00014341483,0.00041078078,0.000007260751],"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":[0.00076359115,0.00023547841,0.0023794842,0.000028331056,0.00020446492,0.00007147773,0.006716574,0.0058048307,0.907918,0.000013149714,0.042243205,0.033621438],"study_design_scores_gemma":[0.009974801,0.0024936507,0.6225443,0.000632666,0.0011900777,0.013356497,0.011246598,0.17894548,0.023156978,0.061841305,0.0723544,0.0022632335],"about_ca_topic_score_codex":0.00010206464,"about_ca_topic_score_gemma":0.000035408644,"teacher_disagreement_score":0.884761,"about_ca_system_score_codex":0.00013448595,"about_ca_system_score_gemma":0.000021458754,"threshold_uncertainty_score":0.7316786},"labels":[],"label_agreement":null},{"id":"W4285139587","doi":"10.1002/ppj2.20042","title":"Segmentation of vegetation and microplots in aerial agriculture images: A survey","year":2022,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Vegetation (pathology); Segmentation; Thresholding; Computer science; Image segmentation; Geography; Agricultural engineering; Remote sensing; Environmental science; Artificial intelligence; Image (mathematics); Engineering","score_opus":0.011202936236477229,"score_gpt":0.19398384614173592,"score_spread":0.1827809099052587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285139587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989594,0.000113055816,0.000034184188,0.00017365572,0.00017360896,0.00012051941,0.000032251985,0.0000041600792,0.00038914065],"genre_scores_gemma":[0.9993412,0.00005300838,0.00039341755,0.000056150784,0.000052359243,8.413915e-7,0.000030118463,0.0000042054767,0.00006871363],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99896747,0.00035105803,0.00019527067,0.00009547195,0.00027182768,0.00011893132],"domain_scores_gemma":[0.9996535,0.00006897224,0.00018260766,0.000056972858,0.000006861871,0.000031094725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007133084,0.00007208706,0.000095295945,0.000024207044,0.00019827207,0.000031070664,0.00013515668,0.000017574446,0.00010779518],"category_scores_gemma":[0.000021148515,0.000041713174,0.000019631041,0.00019891911,0.00005250867,0.0001010655,0.0001014836,0.000259453,0.0000053600247],"study_design_candidate":"observational","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":[0.0001251044,0.000084905136,0.040860195,0.0000062459662,0.000015321655,0.000018016377,0.0061686947,0.028206628,0.9178874,0.0000044835106,0.00497388,0.0016491617],"study_design_scores_gemma":[0.00042484995,0.000067410736,0.9948057,0.000009896308,0.0000102428685,0.0006786286,0.0008299906,0.00024165273,0.0023151224,0.00016419629,0.00037013204,0.000082208295],"about_ca_topic_score_codex":0.00036603468,"about_ca_topic_score_gemma":0.00040668214,"teacher_disagreement_score":0.95394546,"about_ca_system_score_codex":0.00013564773,"about_ca_system_score_gemma":0.000007233183,"threshold_uncertainty_score":0.17010137},"labels":[],"label_agreement":null},{"id":"W4290761358","doi":"10.1002/ppj2.20050","title":"Relationships between roots, the stay‐green phenotype, and agronomic performance in barley and wheat grown in semi‐arid conditions","year":2022,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Plant nutrient uptake and metabolism","field":"Agricultural and Biological Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Montana Wheat and Barley Committee","keywords":"Hordeum vulgare; Seedling; Agronomy; Biology; Arid; Crop; Root system; Yield (engineering); Greenhouse; Poaceae; Crop yield; Ecology","score_opus":0.03455465048621727,"score_gpt":0.19756619688623467,"score_spread":0.16301154640001742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290761358","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99423546,0.001868738,4.5065977e-7,0.002916747,0.0000789037,0.00016261716,0.0004103863,0.000007744821,0.0003189794],"genre_scores_gemma":[0.99787354,0.0015292587,0.0000036926017,0.00010215384,0.00023266351,0.0000127003495,0.00009000898,0.0000010147746,0.00015497846],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990137,0.00021400263,0.00024966453,0.00013168743,0.00015215533,0.00023876403],"domain_scores_gemma":[0.9992882,0.00048367007,0.000101279235,0.000043609645,0.000008796131,0.000074467906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001119665,0.000100767735,0.00015429758,0.000036994246,0.0010304291,0.000069012785,0.00024391297,0.00003226576,0.00011184154],"category_scores_gemma":[0.000018528222,0.000035035035,0.000023392544,0.00021950276,0.00006518724,0.00018357435,0.00012224304,0.00075146236,0.0000039426504],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009545995,0.000055849163,0.9806284,0.000003612373,0.00002485613,0.000010701861,0.0017084664,0.00021047225,0.007349829,0.0012713196,0.00054081535,0.008100228],"study_design_scores_gemma":[0.00022804619,0.000059189373,0.9784832,0.000015002365,0.000018078337,0.00015004152,0.0012475796,0.00014871653,0.000013389256,0.0016556523,0.017888172,0.00009294563],"about_ca_topic_score_codex":0.00019159296,"about_ca_topic_score_gemma":0.0007578439,"teacher_disagreement_score":0.017347356,"about_ca_system_score_codex":0.00003689069,"about_ca_system_score_gemma":0.000014776171,"threshold_uncertainty_score":0.7925332},"labels":[],"label_agreement":null},{"id":"W4312565055","doi":"10.1002/ppj2.20056","title":"Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management","year":2022,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Research Data Management Practices","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Interoperability; Data sharing; Incentive; Reuse; Open science; Data science; Data management; Science policy; Knowledge management; Metadata; Computer science; World Wide Web; Political science; Database; Engineering","score_opus":0.5385673647772101,"score_gpt":0.47994392122279667,"score_spread":0.05862344355441346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312565055","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038866866,0.003559756,0.29487628,0.58739084,0.0017998655,0.009794334,0.027465371,0.00036611076,0.035880588],"genre_scores_gemma":[0.9109232,0.020834874,0.05975131,0.0030203615,0.0012116274,0.0005485183,0.0023800323,0.00007795955,0.0012521483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99562746,0.0005654298,0.00039658032,0.0011898346,0.001312727,0.00090798514],"domain_scores_gemma":[0.9928653,0.0012758822,0.0004643374,0.005019457,0.00007921016,0.000295804],"candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.033910386,0.00013993839,0.00014071763,0.0013879652,0.004817024,0.005507745,0.024797613,0.000014607035,0.000024782357],"category_scores_gemma":[0.0031196163,0.00011211975,0.000014252473,0.0019451104,0.00026249216,0.020708865,0.030635722,0.0011560721,0.0000058064766],"study_design_candidate":"theoretical_or_conceptual","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":[0.00020790796,0.00020854591,0.0018016443,0.00007358636,0.00012049795,0.00006307596,0.0015545397,0.001519279,0.0005708713,0.9517108,0.024220861,0.017948348],"study_design_scores_gemma":[0.0013651727,0.00028997642,0.022738878,0.00012237912,0.000044791246,0.00089982385,0.015295151,0.24419053,0.0000044378653,0.029234266,0.6853322,0.0004824421],"about_ca_topic_score_codex":0.00030343296,"about_ca_topic_score_gemma":0.00010575924,"teacher_disagreement_score":0.9224766,"about_ca_system_score_codex":0.0005027711,"about_ca_system_score_gemma":0.0008023086,"threshold_uncertainty_score":0.99647856},"labels":[],"label_agreement":null},{"id":"W4327518955","doi":"10.1002/ppj2.20065","title":"A neural network for phenotyping <i>Fusarium</i>‐damaged kernels (FDKs) in wheat and its impact on genomic selection accuracy","year":2023,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Centre","funders":"U.S. Department of Agriculture","keywords":"Artificial neural network; Heritability; Fusarium; Artificial intelligence; Selection (genetic algorithm); Computer science; Biology; Trait; Machine learning; Biotechnology; Pattern recognition (psychology); Genetics","score_opus":0.034970140675052074,"score_gpt":0.2557517173554065,"score_spread":0.22078157668035445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327518955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968814,0.00047162906,0.0000032349362,0.0016903842,0.00024501115,0.0003878618,0.00011202734,0.000049392394,0.00015904085],"genre_scores_gemma":[0.99699,0.0005664267,0.000016044463,0.00032417834,0.0018863879,0.00001860158,0.0000757357,0.0000023419593,0.00012023757],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986969,0.00014827143,0.00027548333,0.00020457001,0.00015465198,0.0005201095],"domain_scores_gemma":[0.9988686,0.00079509855,0.00015343267,0.00003576839,0.000035265897,0.000111823654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007781498,0.00019052546,0.00022362126,0.000023963887,0.0006163243,0.0001903427,0.00026886756,0.000078070676,0.000062702165],"category_scores_gemma":[0.00006591428,0.000056612495,0.0001104068,0.00046858253,0.000015866433,0.0001605298,0.000056589713,0.00043557465,0.00002210753],"study_design_candidate":"observational","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":[0.0015174011,0.00014584835,0.013310755,0.000024163966,0.0001564018,0.000029433573,0.0014065751,0.024128688,0.9011139,0.0008536017,0.014569319,0.042743854],"study_design_scores_gemma":[0.0010440801,0.0011649153,0.9526116,0.00012099107,0.00006633778,0.0007198315,0.00053142564,0.030423885,0.0010925407,0.0057751895,0.0058891163,0.0005600735],"about_ca_topic_score_codex":0.000030810952,"about_ca_topic_score_gemma":0.00019459122,"teacher_disagreement_score":0.93930084,"about_ca_system_score_codex":0.00006173379,"about_ca_system_score_gemma":0.000013826034,"threshold_uncertainty_score":0.4740331},"labels":[],"label_agreement":null},{"id":"W4365395410","doi":"10.1002/ppj2.20067","title":"Annual Report 2022: <i>The Plant Phenome Journal</i>","year":2023,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Invertebrate Taxonomy and Ecology","field":"Agricultural and Biological Sciences","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":"Phenome; Citation; Computer science; Information retrieval; Library science; Data science; Biology","score_opus":0.03405466294669376,"score_gpt":0.20098962120306496,"score_spread":0.1669349582563712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365395410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97111803,0.00043915678,0.0000067776205,0.022215962,0.0023987754,0.00020348413,0.0005974092,0.000082001796,0.002938409],"genre_scores_gemma":[0.98039675,0.0015809126,0.000021560589,0.003421541,0.007913544,0.000019207424,0.00020736363,0.000005195445,0.0064339326],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976275,0.00031868496,0.00062648946,0.00022401982,0.00041068517,0.0007926072],"domain_scores_gemma":[0.9985352,0.00049878896,0.0005150138,0.00012440508,0.00009480644,0.0002317527],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029268996,0.0002271992,0.00030137747,0.00003762549,0.002235265,0.00023763148,0.0010229795,0.00011094492,0.0019184691],"category_scores_gemma":[0.00009782156,0.00006168137,0.00020488873,0.00048799807,0.00015259518,0.00026465315,0.0001914947,0.0010636781,0.0007187849],"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":[0.00043353345,0.0002253285,0.0072552357,0.000010545791,0.00041941894,0.0057570306,0.0030235215,0.00041713336,0.027035598,0.0011860309,0.93289864,0.021337975],"study_design_scores_gemma":[0.00032720764,0.00038423395,0.04062184,0.000027224442,0.00006859301,0.04586498,0.0057260464,0.0002859658,0.00012312329,0.005358759,0.9008572,0.0003548008],"about_ca_topic_score_codex":0.000046627993,"about_ca_topic_score_gemma":0.00030965637,"teacher_disagreement_score":0.04010795,"about_ca_system_score_codex":0.00006510969,"about_ca_system_score_gemma":0.000060667757,"threshold_uncertainty_score":0.9990637},"labels":[],"label_agreement":null},{"id":"W4392630812","doi":"10.1002/ppj2.20097","title":"The Height Pole: Measuring plot height using a single‐point LiDAR sensor","year":2024,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Lidar; Plot (graphics); Remote sensing; Geodesy; Point (geometry); Single point; Geology; Environmental science; Geometry; Mathematics; Statistics","score_opus":0.03816340126105266,"score_gpt":0.22307235136402684,"score_spread":0.18490895010297417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392630812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.949204,0.0023696967,0.0067541054,0.009092513,0.0011116142,0.00031596603,0.00003268177,0.00017884973,0.030940559],"genre_scores_gemma":[0.9966012,0.00017184882,0.0012535421,0.0001722119,0.00075188617,9.3398154e-7,0.000001876756,0.000035892357,0.0010106043],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841005,0.00014809477,0.0003177389,0.00021930541,0.00047764974,0.00042717432],"domain_scores_gemma":[0.9991595,0.00024337327,0.00010356288,0.00033674182,0.000010837564,0.00014597405],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009224779,0.0001738624,0.00013069628,0.000038720827,0.0015005062,0.0006203635,0.00040549226,0.00004190189,0.00018381634],"category_scores_gemma":[0.000029002862,0.00008917505,0.000114430644,0.00026032396,0.00021356299,0.00016033599,0.00013182366,0.0005625891,0.00043568158],"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":[0.00016994722,0.00027694338,0.0007772836,0.00003797761,0.000519343,0.00041527677,0.013923183,0.014776401,0.88118804,0.0025335143,0.015770135,0.06961195],"study_design_scores_gemma":[0.00066207215,0.00021307691,0.00725039,0.00064558885,0.0004303772,0.024652112,0.0031465162,0.12746769,0.018758815,0.016585104,0.7989504,0.0012379031],"about_ca_topic_score_codex":0.000086244254,"about_ca_topic_score_gemma":0.000036553767,"teacher_disagreement_score":0.8624292,"about_ca_system_score_codex":0.0003214484,"about_ca_system_score_gemma":0.000035910434,"threshold_uncertainty_score":0.99979943},"labels":[],"label_agreement":null},{"id":"W4398245406","doi":"10.1002/ppj2.20103","title":"Estimating Fusarium head blight severity in winter wheat using deep learning and a spectral index","year":2024,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Fusarium; Index (typography); Winter wheat; Head (geology); Agronomy; Blight; Environmental science; Mathematics; Horticulture; Biology; Computer science","score_opus":0.02293843411685579,"score_gpt":0.28671018001187065,"score_spread":0.26377174589501484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398245406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98756844,0.0045420914,0.003517837,0.00030836515,0.00015762218,0.000025092797,0.000008792647,0.000049830014,0.003821951],"genre_scores_gemma":[0.9975463,0.00010038442,0.0010129825,0.0000362445,0.0008100197,9.919867e-7,0.000005104858,0.00002031257,0.00046766523],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887073,0.000037879465,0.00030924886,0.00019944289,0.0002093686,0.00037330625],"domain_scores_gemma":[0.999544,0.00016835137,0.00008573775,0.00009038092,0.000016096707,0.0000954061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037030014,0.00017669625,0.00024796312,0.00021932492,0.00025668633,0.00037360602,0.0001725755,0.0000739869,0.000909759],"category_scores_gemma":[0.0000549794,0.00012316063,0.000075718504,0.00036531087,0.00006136671,0.0002011065,0.00007703086,0.001283424,0.000008071627],"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":[0.0005566551,0.00029983805,0.30153164,0.0009907428,0.001374921,0.002021559,0.016524766,0.016779263,0.6513819,0.00032345453,0.00042316102,0.007792095],"study_design_scores_gemma":[0.001096539,0.00007997297,0.0052386005,0.00074006146,0.0005153449,0.011169261,0.003867283,0.9456044,0.025756588,0.0036111267,0.0015688369,0.00075199304],"about_ca_topic_score_codex":0.000055623346,"about_ca_topic_score_gemma":0.000033225944,"teacher_disagreement_score":0.92882514,"about_ca_system_score_codex":0.0002014495,"about_ca_system_score_gemma":0.00005016769,"threshold_uncertainty_score":0.99612236},"labels":[],"label_agreement":null},{"id":"W4404883626","doi":"10.1002/ppj2.70012","title":"Hyperspectral sensing for high‐throughput chloride detection in grapevines","year":2024,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Hyperspectral imaging; Throughput; Remote sensing; Environmental science; Computer science; Artificial intelligence; Geology; Telecommunications","score_opus":0.024048086883800363,"score_gpt":0.26017218272911696,"score_spread":0.2361240958453166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404883626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877354,0.0034408118,0.005176774,0.0011909382,0.0005705355,0.000057031,0.000042779327,0.00009280462,0.001692956],"genre_scores_gemma":[0.9965215,0.00042009115,0.00038144158,0.000055282115,0.0017502162,0.000003099288,0.000009165401,0.000023670453,0.00083555625],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990282,0.000012639627,0.00027860605,0.00018211217,0.00016844491,0.00032998004],"domain_scores_gemma":[0.9994243,0.00028754378,0.000075841315,0.00012886658,0.000028391321,0.00005508383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002894789,0.0001525228,0.00020863311,0.00020713583,0.00021606476,0.00019861768,0.00017307661,0.00006595891,0.000256146],"category_scores_gemma":[0.00006479471,0.00010184694,0.00013375183,0.00043043788,0.00004001009,0.00014181601,0.00002060121,0.0004900364,0.000012830286],"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":[0.000500935,0.000084672836,0.00024466365,0.0002521751,0.0006078864,0.0001968753,0.0014579429,0.0003494448,0.98321956,0.0023024525,0.00093987794,0.00984351],"study_design_scores_gemma":[0.00078694127,0.000086913555,0.0003247317,0.00016703161,0.00051487697,0.002792754,0.0021676987,0.0059258984,0.9578909,0.023051407,0.0058970777,0.00039375687],"about_ca_topic_score_codex":0.00006672279,"about_ca_topic_score_gemma":0.000058286783,"teacher_disagreement_score":0.025328651,"about_ca_system_score_codex":0.00020851132,"about_ca_system_score_gemma":0.00004826702,"threshold_uncertainty_score":0.41531977},"labels":[],"label_agreement":null},{"id":"W4408320353","doi":"10.1002/ppj2.70020","title":"Use of arduino‐based potentiometric sensors to measure changes in leaf apoplastic pH in common bean ( <i>Phaseolus vulgaris</i> L.)","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Phaseolus; Potentiometric titration; Measure (data warehouse); Arduino; Apoplast; Horticulture; Chemistry; Botany; Biology; Computer science; Data mining; Electrode","score_opus":0.03782402823637851,"score_gpt":0.23973410443793386,"score_spread":0.20191007620155535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408320353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99523914,0.00018926135,0.0022228016,0.0012876877,0.00011458442,0.00013315737,0.000072076255,0.000024441995,0.0007168717],"genre_scores_gemma":[0.99903995,0.000027687562,0.00011099924,0.00022131545,0.00007576101,0.0000031896352,0.000011809429,0.000014891545,0.00049438915],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984395,0.000088111425,0.0005316966,0.00018585255,0.00034340782,0.00041142176],"domain_scores_gemma":[0.9986623,0.0007758054,0.00011914565,0.00023674253,0.000055096552,0.00015093898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004921147,0.00020389218,0.00045418474,0.00065600977,0.00006387971,0.00004234175,0.00032525294,0.00011241692,0.00007235389],"category_scores_gemma":[0.0005559661,0.00015605842,0.00009990114,0.0014728145,0.00004930582,0.00006246091,0.00005823682,0.00080894545,0.00000817886],"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":[0.0012081055,0.0005825243,0.011114374,0.00032107852,0.00021413987,0.00031399247,0.00067110965,0.4251702,0.5577861,0.00039430894,0.0010295977,0.0011944214],"study_design_scores_gemma":[0.0076373876,0.0003701259,0.017674295,0.0033645742,0.0005774502,0.00037843222,0.0014829709,0.47026843,0.49023238,0.0010421692,0.005341592,0.0016301862],"about_ca_topic_score_codex":0.00007205085,"about_ca_topic_score_gemma":0.000113583425,"teacher_disagreement_score":0.06755374,"about_ca_system_score_codex":0.0001725316,"about_ca_system_score_gemma":0.000043513268,"threshold_uncertainty_score":0.63638777},"labels":[],"label_agreement":null},{"id":"W4411615525","doi":"10.1002/ppj2.70024","title":"Foliar and berry hyperspectral reflectance predicts winegrape berry composition across developmental stages and varieties","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Institute of Food and Agriculture","keywords":"Berry; Hyperspectral imaging; Reflectivity; Environmental science; Horticulture; Botany; Biology; Geography; Remote sensing; Optics; Physics","score_opus":0.029060091360010174,"score_gpt":0.2674247696762153,"score_spread":0.23836467831620514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411615525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99068356,0.001374174,0.0000031734505,0.006828987,0.00008018405,0.00014891449,0.000077324454,0.000026167614,0.0007775471],"genre_scores_gemma":[0.9970514,0.0013562832,0.00009118835,0.00026291827,0.00021178952,0.0000062961576,0.000022588369,7.207681e-7,0.0009968098],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99890167,0.00008800152,0.0002059691,0.00018110174,0.00024396453,0.00037930082],"domain_scores_gemma":[0.99954,0.00017961014,0.00006169249,0.000023661863,0.00006811051,0.00012697655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003143036,0.0001471074,0.00016477764,0.000010492814,0.0011481866,0.0003480248,0.00018839967,0.00005738977,0.000042275304],"category_scores_gemma":[0.000028856035,0.000043256226,0.000039455586,0.00017778378,0.00019994407,0.00022810824,0.00012697124,0.00034307555,0.0000033357085],"study_design_candidate":"observational","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":[0.0003102729,0.000080259946,0.010371499,0.000027202132,0.0001351733,0.000030301742,0.0030180914,0.0000033021324,0.963451,0.0029332342,0.0014862993,0.018153343],"study_design_scores_gemma":[0.0005218063,0.00040421984,0.9700159,0.00014208087,0.00004990457,0.0011769518,0.0089460965,0.00009480363,0.006641494,0.006286762,0.0054014134,0.00031855612],"about_ca_topic_score_codex":0.00004946109,"about_ca_topic_score_gemma":0.00018503045,"teacher_disagreement_score":0.95964444,"about_ca_system_score_codex":0.000048663493,"about_ca_system_score_gemma":0.000015197395,"threshold_uncertainty_score":0.88310397},"labels":[],"label_agreement":null},{"id":"W4412141372","doi":"10.1002/ppj2.70032","title":"Differences in apple fruit shape are independent of fruit size","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Plant Physiology and Cultivation Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University; Acadia University","funders":"Division of Integrative Organismal Systems; Canada Research Chairs","keywords":"Horticulture; Mathematics; Biology","score_opus":0.03767406824390116,"score_gpt":0.22154103902097658,"score_spread":0.18386697077707542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412141372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965749,0.00034620654,0.0000016591567,0.0018108555,0.00011780779,0.00007858834,0.00013531602,0.000008311747,0.0009264043],"genre_scores_gemma":[0.9990036,0.00025930416,0.0000048356915,0.0002686569,0.000096043324,0.0000059164613,0.000010062777,2.3755469e-7,0.00035133178],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992206,0.00009941468,0.00025992395,0.00010663528,0.00014076364,0.00017263333],"domain_scores_gemma":[0.999078,0.0006451617,0.00018557375,0.0000305466,0.000035235298,0.000025509142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002822196,0.000093561146,0.00023159907,0.000015091283,0.0002459726,0.000023291332,0.0003305461,0.000051907045,0.0003045198],"category_scores_gemma":[0.00006510673,0.000027221142,0.00005533554,0.00020507419,0.0000689815,0.00005554566,0.000082493796,0.00025456067,0.000007807896],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045219244,0.00038737076,0.5906243,0.000023240893,0.00024012968,0.000020345324,0.0013027309,0.000023890765,0.38958544,0.0027202645,0.005944112,0.008676033],"study_design_scores_gemma":[0.00015632648,0.000039951385,0.99217576,0.000064756256,0.000010359281,0.0000144034,0.0022601215,0.000023112407,0.00044921544,0.0033775775,0.0013632993,0.00006511096],"about_ca_topic_score_codex":0.00003397706,"about_ca_topic_score_gemma":0.00030576382,"teacher_disagreement_score":0.40155149,"about_ca_system_score_codex":0.000013383131,"about_ca_system_score_gemma":0.000009808832,"threshold_uncertainty_score":0.33342782},"labels":[],"label_agreement":null},{"id":"W4412141841","doi":"10.1002/ppj2.70031","title":"Prediction of symbiotic nitrogen fixation in common bean ( <i>Phaseolus vulgaris</i> L.) using unmanned aerial system remote sensing","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Michigan Bean Commission; Michigan Crop Improvement Association; AgBioResearch, Michigan State University; Michigan Department of Agriculture and Rural Development","keywords":"Phaseolus; Nitrogen fixation; Remote sensing; Environmental science; Nitrogen; Biology; Botany; Chemistry; Geography","score_opus":0.0317181168272696,"score_gpt":0.21521695325009552,"score_spread":0.1834988364228259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412141841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99847025,0.00012895785,0.0003452864,0.00019889031,0.00037394385,0.0001509185,0.000051201703,0.000013328037,0.00026725236],"genre_scores_gemma":[0.9994911,0.0000183884,0.00009916796,0.00003543658,0.0002975986,1.0678436e-7,0.000044652417,7.8663226e-7,0.000012752454],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904615,0.00016480341,0.00037891252,0.000098189324,0.00015946051,0.00015251101],"domain_scores_gemma":[0.9995317,0.00009441336,0.00024396258,0.000043752003,0.00005593717,0.000030273854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049611327,0.00008504885,0.00015700053,0.000031076077,0.0002560671,0.000056142144,0.0001280849,0.000058761158,0.0000070344345],"category_scores_gemma":[0.000018538916,0.00003393306,0.00005039995,0.00026878776,0.0000261223,0.00005275619,0.000030710453,0.00014797196,9.111629e-7],"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":[0.00022889377,0.00004117801,0.008002023,0.00002638667,0.000046740355,0.000006182373,0.000395539,0.001528971,0.972809,0.0001962727,0.00010977727,0.01660899],"study_design_scores_gemma":[0.0040510586,0.00097502436,0.27420932,0.003195824,0.0005309464,0.0010482015,0.014380866,0.56823665,0.1102202,0.021532085,0.00085794856,0.00076186436],"about_ca_topic_score_codex":0.0003626206,"about_ca_topic_score_gemma":0.00018237405,"teacher_disagreement_score":0.8625888,"about_ca_system_score_codex":0.000097188975,"about_ca_system_score_gemma":0.00001666835,"threshold_uncertainty_score":0.1969487},"labels":[],"label_agreement":null},{"id":"W4413024583","doi":"10.1002/ppj2.70034","title":"Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lethbridge College; Agriculture and Agri-Food Canada; McGill University","funders":"Agriculture and Agri-Food Canada; Kirkhouse Trust; United States Agency for International Development; National Science Foundation","keywords":"Phenomics; Biotechnology; Genetic gain; Computer science; Biology; Medicine; Genetic variation; Environmental health; Genetics; Genomics","score_opus":0.03912880358489513,"score_gpt":0.26903187496592845,"score_spread":0.22990307138103333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413024583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9702098,0.00011930539,0.0019706334,0.0012884408,0.00043561845,0.0001853203,0.000072119925,0.000019668389,0.025699124],"genre_scores_gemma":[0.99803734,0.00020039066,0.000107120635,0.0010895194,0.00010883048,0.000013316808,0.000011746878,0.000009108061,0.00042265767],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987412,0.000051167382,0.00033505942,0.00018480165,0.0002205724,0.00046718432],"domain_scores_gemma":[0.9995275,0.00009352778,0.000099467296,0.0001520656,0.000005260448,0.00012215153],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062862627,0.00013795227,0.00017015933,0.000110199595,0.0003754145,0.0002966477,0.0006528432,0.00003598789,0.007934201],"category_scores_gemma":[0.000040994833,0.00010169922,0.000046059115,0.0003782352,0.000045069566,0.00021194566,0.00039455175,0.0002648368,0.00025295868],"study_design_candidate":"observational","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":[0.0007459115,0.0005229861,0.3947595,0.000100437865,0.00016031877,0.00030553786,0.015634233,0.04135088,0.41370845,0.004101367,0.116011314,0.01259908],"study_design_scores_gemma":[0.0038003644,0.00032345433,0.8287185,0.00096709013,0.00012807785,0.001125007,0.043578792,0.006624807,0.026544984,0.0060372893,0.08055203,0.0015996005],"about_ca_topic_score_codex":0.0001765697,"about_ca_topic_score_gemma":0.0012120553,"teacher_disagreement_score":0.433959,"about_ca_system_score_codex":0.0010332803,"about_ca_system_score_gemma":0.00002139754,"threshold_uncertainty_score":0.9929727},"labels":[],"label_agreement":null},{"id":"W4413361465","doi":"10.1002/ppj2.70040","title":"Dissecting lentil crop growth in contrasting environments using digital imaging and genome‐wide association studies","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Saskatchewan Pulse Growers; Western Grains Research Foundation; Genome Canada","keywords":"Crop; Genome-wide association study; Biology; Association (psychology); Agronomy; Genetics; Psychology; Genotype; Single-nucleotide polymorphism; Gene","score_opus":0.021488209026036653,"score_gpt":0.2100647617541588,"score_spread":0.18857655272812215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413361465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99452853,0.0038063587,0.000021734262,0.0011606078,0.00012069278,0.0000666947,0.000018304563,0.000004192875,0.00027288386],"genre_scores_gemma":[0.99858004,0.00093086954,0.000016237711,0.00015224953,0.00010863372,0.0000014325018,0.0000035680987,7.29856e-7,0.0002062361],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992526,0.00003776928,0.0002157618,0.00012178169,0.00013310037,0.00023896074],"domain_scores_gemma":[0.99933726,0.00047306888,0.0001368773,0.000015271833,0.000009471475,0.000028081773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367981,0.000102303195,0.00014609926,0.000013744093,0.0005868388,0.00013470778,0.00009379041,0.000017821323,0.000007791086],"category_scores_gemma":[0.00020528739,0.000038451155,0.000031422947,0.00007668238,0.000056336055,0.00012608239,0.00014734862,0.00015471202,0.0000015883036],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013109943,0.000024106717,0.96303654,0.0000041694825,0.000066658096,0.0000055497867,0.00041626347,0.00010720432,0.034540996,0.0000053468916,0.000021227359,0.0017588114],"study_design_scores_gemma":[0.00015825522,0.000016568494,0.9906654,0.000049359718,0.000028844448,0.000019215071,0.00729537,0.00013263394,0.00014432872,0.0009933182,0.0004082645,0.00008846373],"about_ca_topic_score_codex":0.00002463243,"about_ca_topic_score_gemma":0.000021536434,"teacher_disagreement_score":0.034396667,"about_ca_system_score_codex":0.0001628941,"about_ca_system_score_gemma":0.0000019976878,"threshold_uncertainty_score":0.45135492},"labels":[],"label_agreement":null},{"id":"W4415729868","doi":"10.1002/ppj2.70046","title":"Leaf hyperspectral reflectance detects pre‐visual stress to <i>Fusarium</i> wilt in strawberries","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing in Agriculture","field":"Environmental 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":"University of New Brunswick","funders":"Agricultural Research Service; National Institute of Food and Agriculture","keywords":"Hyperspectral imaging; Multispectral image; Photochemical Reflectance Index; Reflectivity; Principal component analysis; Cultivar; Stomatal conductance; Chlorophyll; Canopy","score_opus":0.007639663918805115,"score_gpt":0.23581221769341057,"score_spread":0.22817255377460546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415729868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98012906,0.00015294929,0.00019926787,0.0028798305,0.0003898481,0.00021972458,0.0000165224,0.00003139959,0.015981384],"genre_scores_gemma":[0.99639404,0.00006495372,0.0011257664,0.00062582386,0.00017589107,0.0000013902677,0.0000031285854,0.000010448929,0.0015985443],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99841857,0.00012780272,0.00030217416,0.00026498057,0.00038813715,0.00049832516],"domain_scores_gemma":[0.99943584,0.0001036373,0.000102912265,0.00022027173,0.000011035038,0.00012630879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035768998,0.00020036129,0.0002058014,0.00006710659,0.00030372856,0.00016647173,0.00056118984,0.000072072355,0.00010813233],"category_scores_gemma":[0.000068229085,0.000124139,0.000058574857,0.0005211006,0.00013677204,0.0001870317,0.00016865734,0.0006997006,0.00006773992],"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":[0.00063106295,0.00033840985,0.024486208,0.000023509876,0.00009538492,0.00027594724,0.011175688,0.08207869,0.8552388,0.0004150713,0.017090578,0.008150661],"study_design_scores_gemma":[0.0027298222,0.00074727286,0.81419057,0.0009206675,0.00016076938,0.0025939448,0.0058001364,0.00553842,0.13458027,0.012003374,0.018996801,0.0017379756],"about_ca_topic_score_codex":0.00015333688,"about_ca_topic_score_gemma":0.0016319896,"teacher_disagreement_score":0.7897043,"about_ca_system_score_codex":0.00041244738,"about_ca_system_score_gemma":0.000036289013,"threshold_uncertainty_score":0.50622416},"labels":[],"label_agreement":null},{"id":"W4416063131","doi":"10.1002/ppj2.70047","title":"UAV‐based high‐throughput phenotyping for crop growth analysis and seed yield prediction in a nested association mapping population of lentils","year":2025,"lang":"en","type":"article","venue":"The Plant Phenome Journal","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Genome Prairie; Saskatchewan Pulse Growers; Western Grains Research Foundation; Canada First Research Excellence Fund; University of Saskatchewan; Genome Canada","keywords":"Crop; Canopy; Regression; Crop yield; Population; Regression analysis; Yield (engineering)","score_opus":0.010204129701832047,"score_gpt":0.1987600032974547,"score_spread":0.18855587359562265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416063131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731689,0.000031309486,0.025151325,0.0009464889,0.00017117609,0.00023096248,0.000027441141,0.000014272787,0.00025810456],"genre_scores_gemma":[0.9979756,0.000016469343,0.0016436804,0.000090944726,0.000058770125,0.0000013083885,0.00004027551,0.0000042468137,0.00016869731],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989654,0.00010418601,0.00036855583,0.00015058173,0.00023684328,0.00017445802],"domain_scores_gemma":[0.9992749,0.00020798817,0.0003757258,0.00008687376,0.000027476148,0.000027021124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000796718,0.00009663452,0.00020882166,0.00015993846,0.0002029553,0.00006082457,0.00010431816,0.00007883864,0.00002193204],"category_scores_gemma":[0.00017371701,0.00006727864,0.00007296619,0.0007656109,0.000023521485,0.00017130029,0.000033625765,0.00018465443,0.0000010301891],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009929814,0.000059783757,0.90481234,0.000032461718,0.00019291822,0.0000012209794,0.000937793,0.04743783,0.044591863,0.00007168426,0.00037397782,0.0013888337],"study_design_scores_gemma":[0.00037850143,0.000016746291,0.9308318,0.000095508796,0.00022211992,0.000004310779,0.00010606567,0.06674755,0.00047660453,0.0010267801,0.000030359815,0.000063661224],"about_ca_topic_score_codex":0.0008086858,"about_ca_topic_score_gemma":0.0005375111,"teacher_disagreement_score":0.044115257,"about_ca_system_score_codex":0.00039013044,"about_ca_system_score_gemma":0.00000937259,"threshold_uncertainty_score":0.27435434},"labels":[],"label_agreement":null}]}