{"meta":{"query_hash":"bde902922a69","filters":{"venue":"Open Data Journal for Agricultural Research"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/bde902922a69","api":"https://metacan.xera.ac/api/v1/cohort?venue=Open+Data+Journal+for+Agricultural+Research"},"results":[{"id":"W2270683481","doi":"10.18174/odjar.v1i1.14746","title":"Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations","year":2016,"lang":"en","type":"article","venue":"Open Data Journal for Agricultural Research","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Plant Biotechnology Institute; University of Guelph; University of Alberta","funders":"","keywords":"Benchmark (surveying); Data set; Set (abstract data type); Field (mathematics); Computer science; Data mining; Mathematics; Artificial intelligence; Cartography; Geography; Programming language","score_opus":0.7336355095892637,"score_gpt":0.525251031526756,"score_spread":0.2083844780625077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270683481","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61389565,0.00088587933,0.035878714,0.0007902657,0.00020020537,0.0008684864,0.31798032,0.004418381,0.02508205],"genre_scores_gemma":[0.6999675,0.00033140118,0.026010007,0.00021939006,0.000031228978,0.0011501928,0.26938495,0.0005022727,0.0024030332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991636,0.00034756362,0.000073781826,0.00014687942,0.00018333856,0.00008485372],"domain_scores_gemma":[0.9958264,0.0017232519,0.00038115206,0.00094763044,0.0009113142,0.00021011372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028082859,0.0014000016,0.0008190485,0.0011692613,0.00050893036,0.000829626,0.0023532584,0.0012058561,0.0044975984],"category_scores_gemma":[0.0050426167,0.00041777414,0.00070577307,0.0023728814,0.00040546805,0.00081127626,0.00059597223,0.0010289389,0.0011191309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063827843,0.0007940542,0.010907646,0.00044535563,0.00019163534,0.00013644093,0.000040748248,0.93694395,0.0021667231,0.0027703878,0.03267032,0.012294365],"study_design_scores_gemma":[0.0009158917,0.00053041405,0.02244147,0.00006019659,0.0001076355,0.000080940044,0.00009898387,0.94215924,0.0070467517,0.0060091116,0.020464411,0.000084942796],"about_ca_topic_score_codex":0.021092243,"about_ca_topic_score_gemma":0.015297021,"teacher_disagreement_score":0.021092243,"about_ca_system_score_codex":0.0011797493,"about_ca_system_score_gemma":0.0008507866,"threshold_uncertainty_score":0.04193896},"labels":[],"label_agreement":null},{"id":"W2598134958","doi":"10.18174/odjar.v3i1.15766","title":"The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations","year":2017,"lang":"en","type":"article","venue":"Open Data Journal for Agricultural Research","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":16,"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":"Heat stress; Field (mathematics); Winter wheat; Environmental science; Agronomy; Atmospheric sciences; Physics; Biology; Mathematics","score_opus":0.5061985508496625,"score_gpt":0.519167243708133,"score_spread":0.012968692858470576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598134958","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":[0.93323535,0.00017934175,0.01717978,0.0004234266,0.000090005226,0.0006535539,0.038157962,0.0007536642,0.009327032],"genre_scores_gemma":[0.93070626,0.00016808289,0.037113532,0.00016988392,0.000027023067,0.0015689736,0.027499435,0.00016857588,0.0025782578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994497,0.00033649427,0.000020484023,0.00008649844,0.000052060394,0.000054831366],"domain_scores_gemma":[0.9977131,0.0014648439,0.00018312778,0.00033123803,0.00017128415,0.00013637223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002347507,0.00091190316,0.0006137336,0.0006009151,0.00050510763,0.0004911586,0.001786533,0.0010000397,0.0027922513],"category_scores_gemma":[0.001836859,0.00044522693,0.0011473037,0.00097516365,0.0005794849,0.00052523153,0.00053849915,0.0013095975,0.00031266527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082083594,0.00075442763,0.012504573,0.00013576123,0.00019934647,0.00012228354,0.00007837125,0.96774685,0.002181637,0.003232895,0.0074543,0.004768691],"study_design_scores_gemma":[0.00065503985,0.00045562643,0.014512585,0.000013367706,0.00010426574,0.000026456753,0.000068207715,0.97608256,0.0022641448,0.0025413118,0.0032164906,0.000059871752],"about_ca_topic_score_codex":0.024574539,"about_ca_topic_score_gemma":0.024679007,"teacher_disagreement_score":0.024574539,"about_ca_system_score_codex":0.0012451777,"about_ca_system_score_gemma":0.00064676243,"threshold_uncertainty_score":0.048862994},"labels":[],"label_agreement":null},{"id":"W2797156798","doi":"10.18174/odjar.v4i0.15830","title":"The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations","year":2018,"lang":"en","type":"article","venue":"Open Data Journal for Agricultural Research","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":9,"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":"Frost (temperature); Environmental science; Relative humidity; Cultivar; Sowing; Growing season; Crop; Air temperature; Irrigation; Winter wheat; Agronomy; Growing degree-day; Humidity; Field experiment; Atmospheric sciences; Meteorology; Geography; Biology","score_opus":0.4417984339113431,"score_gpt":0.5017625604419691,"score_spread":0.059964126530625994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797156798","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":[0.9753146,0.00007315378,0.012038732,0.00013020703,0.000035189925,0.00042536634,0.0067423247,0.00035893059,0.0048815045],"genre_scores_gemma":[0.97145283,0.00007441272,0.021908706,0.000070465714,0.000013000532,0.000741725,0.0041206866,0.00008290286,0.0015352843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.00019030091,0.000011430747,0.000052443167,0.000039334456,0.00003427052],"domain_scores_gemma":[0.9980715,0.0012904287,0.00018522098,0.00022457012,0.000121047975,0.00010723111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018603597,0.0006109952,0.0004922553,0.0004038654,0.00041258684,0.0003333719,0.0013316605,0.0006280842,0.002586825],"category_scores_gemma":[0.0013606539,0.00029784912,0.0008505912,0.00059793977,0.0004410162,0.00044157906,0.00046867863,0.0009064258,0.00019583578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011755889,0.0015267172,0.018461911,0.00019421995,0.00015263613,0.00021362076,0.00009852589,0.95747554,0.00615476,0.0030403151,0.004968926,0.0065372502],"study_design_scores_gemma":[0.00060902815,0.00097074185,0.015628455,0.000009584894,0.00006588842,0.000029205428,0.00007177491,0.97534424,0.0040189456,0.001370312,0.0018371757,0.000044557513],"about_ca_topic_score_codex":0.015550785,"about_ca_topic_score_gemma":0.019068034,"teacher_disagreement_score":0.015550785,"about_ca_system_score_codex":0.00084928534,"about_ca_system_score_gemma":0.0004070728,"threshold_uncertainty_score":0.030920565},"labels":[],"label_agreement":null},{"id":"W4385265559","doi":"10.18174/odjar.v9i0.18573","title":"A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations","year":2023,"lang":"en","type":"article","venue":"Open Data Journal for Agricultural Research","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":6,"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":"Secretaría de Ciencia y Técnica, Universidad de Buenos Aires; Comisión Nacional de Investigación Científica y Tecnológica; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement","keywords":"Agronomy; Crop simulation model; Cultivar; Population; Crop; Irrigation; Environmental science; Anthesis; Crop yield; Agriculture; Simulation modeling; Biology; Yield (engineering); Biomass (ecology); Trait; Mathematics; Ecology; Demography","score_opus":0.359949964393783,"score_gpt":0.45055590118829864,"score_spread":0.09060593679451562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385265559","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":[0.99185914,0.00007916021,0.0029895387,0.00012684254,0.000019376135,0.00007473504,0.0031095117,0.00012469658,0.0016169772],"genre_scores_gemma":[0.98667544,0.000065760345,0.0078395335,0.00006460174,0.000011408097,0.00020778085,0.004448902,0.000038889844,0.0006477044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997818,0.00009821173,0.000012028189,0.000054535853,0.000019963145,0.00003357403],"domain_scores_gemma":[0.9975752,0.0017758793,0.00012472768,0.00019549516,0.00021229727,0.00011633773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011070959,0.0007020041,0.00055783725,0.000376488,0.00041883002,0.0004979287,0.0011059985,0.0011463375,0.0013069105],"category_scores_gemma":[0.0017116977,0.0003447038,0.0010485577,0.0004893519,0.0004393751,0.00053391315,0.00045673802,0.0012410318,0.00014223167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026326394,0.00038635437,0.010361382,0.00006457858,0.00008402762,0.000069789814,0.000038895592,0.98423463,0.0011507189,0.0005723428,0.0010383846,0.0017355714],"study_design_scores_gemma":[0.00012402567,0.00016581123,0.006028384,0.0000055570085,0.000027383012,0.000009168268,0.000044157394,0.992057,0.0007381215,0.0003863003,0.0004007028,0.000013406556],"about_ca_topic_score_codex":0.01904824,"about_ca_topic_score_gemma":0.01986535,"teacher_disagreement_score":0.01904824,"about_ca_system_score_codex":0.0009209974,"about_ca_system_score_gemma":0.00044890784,"threshold_uncertainty_score":0.03787476},"labels":[],"label_agreement":null}]}