{"meta":{"query_hash":"eb841f3378a4","filters":{"venue":"Journal of Agricultural Meteorology"},"cohort_total":10,"direct_labels_cover":0,"predictions_cover":10,"exported":10,"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/eb841f3378a4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Agricultural+Meteorology"},"results":[{"id":"W1649915311","doi":"10.14877/agrmet2.sp09.0.225.0","title":"Asian food systems under dietary transition and climatic changes","year":2009,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Pacific and Southeast Asian Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Nutrition transition; Environmental science; Food systems; Geography; Food security; Biology; Agriculture","score_opus":0.02862619533512759,"score_gpt":0.26918357366146967,"score_spread":0.24055737832634208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1649915311","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":[0.9989814,0.00004613996,0.000017272498,0.00015556252,0.000002698595,0.0000023120926,0.00010035675,0.0000014900697,0.0006926112],"genre_scores_gemma":[0.9997354,0.000044107797,0.00001329267,0.000020197931,0.0000018211904,0.0000018974082,0.00007654293,4.7320577e-7,0.0001062822],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998161,0.000035100726,0.000011398144,0.000029184563,0.000012753902,0.00009546627],"domain_scores_gemma":[0.99954695,0.000044968365,0.00015269346,0.000032166445,0.00007403241,0.00014928852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044541986,0.00015634524,0.00024204307,0.000718585,0.00072818925,0.0016467455,0.00035203263,0.00045425896,0.002613616],"category_scores_gemma":[0.00066715677,0.00020603686,0.00035548757,0.0014880167,0.0008686177,0.0011402467,0.0010997159,0.00046533262,0.0001997756],"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":[0.0007989124,0.00015935834,0.97436315,0.000051336014,0.000231688,0.00079426845,0.0077596176,0.0009835918,0.0026419898,0.0041083954,0.0006282222,0.0074793403],"study_design_scores_gemma":[0.000011344947,0.0000596096,0.98752594,0.000005256163,0.00003904096,0.00009270312,0.010533251,0.000481325,0.00010794017,0.0006312975,0.0005047962,0.0000075831085],"about_ca_topic_score_codex":0.047711857,"about_ca_topic_score_gemma":0.058318704,"teacher_disagreement_score":0.047711857,"about_ca_system_score_codex":0.0014926937,"about_ca_system_score_gemma":0.000988376,"threshold_uncertainty_score":0.09486824},"labels":[],"label_agreement":null},{"id":"W1963868408","doi":"10.2480/agrmet.65.1.2","title":"Seasonal and Annual Water Balance of Agricultural Land in Tokachi, Hokkaido, Japan","year":2009,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":3,"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 Calgary","funders":"","keywords":"Snowmelt; Snow; Environmental science; Precipitation; Water balance; Hydrometeorology; Leaching (pedology); Hydrology (agriculture); Period (music); Snow cover; Groundwater; Soil water; Atmospheric sciences; Climatology; Soil science; Geology; Meteorology; Geography","score_opus":0.0037827609905685313,"score_gpt":0.19433516388548275,"score_spread":0.1905524028949142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963868408","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":[0.9992111,0.00011118803,0.00007234329,0.000018102957,0.0000030848473,0.000004379412,0.00027575312,0.000005466174,0.00029859645],"genre_scores_gemma":[0.9988336,0.00011191653,0.00019714588,0.00001116538,0.0000059356653,0.000011490227,0.0004998765,0.0000030389965,0.00032581418],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99989414,0.000010564121,0.000010929295,0.000031357973,0.000023442,0.000029471861],"domain_scores_gemma":[0.99979895,0.000021697331,0.000051476127,0.00000879825,0.00007380397,0.00004525124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017211586,0.00028009186,0.00027431865,0.0006917245,0.0004828159,0.00039160738,0.00020901777,0.00022066022,0.000698438],"category_scores_gemma":[0.00020949103,0.00017166891,0.0001503525,0.0010259985,0.0002680805,0.00045373384,0.0002456942,0.00010595587,0.00012788617],"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":[0.00015189765,0.0000799966,0.9641587,0.00012303487,0.00017518231,0.0006906138,0.0011799432,0.0018383589,0.020506011,0.00005337319,0.00062256923,0.010420279],"study_design_scores_gemma":[0.0000048756615,0.000015739835,0.9981115,0.0000022741108,0.000024522738,0.000039620616,0.00038029894,0.0007339278,0.00028024483,0.000012105,0.0003903104,0.0000046617156],"about_ca_topic_score_codex":0.07224892,"about_ca_topic_score_gemma":0.16120508,"teacher_disagreement_score":0.07224892,"about_ca_system_score_codex":0.0007115473,"about_ca_system_score_gemma":0.000643039,"threshold_uncertainty_score":0.14365679},"labels":[],"label_agreement":null},{"id":"W1976529257","doi":"10.2480/agrmet.61.131","title":"Effect of Soil Water Content on Carbon Dioxide Flux at a Sparse-Canopy Forest in the Canadian Boreal Ecosystem","year":2005,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Plant Water Relations and Carbon Dynamics","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":true,"ca_institutions":"Queen's University; University of British Columbia; Environment and Climate Change Canada","funders":"National Institute of Advanced Industrial Science and Technology","keywords":"Eddy covariance; Ecosystem respiration; Environmental science; Ecosystem; Water content; Canopy; Soil respiration; Taiga; Boreal ecosystem; Boreal; Hydrology (agriculture); Atmospheric sciences; Soil water; Soil science; Ecology; Forestry; Geography; Geology; Biology","score_opus":0.007873867079681334,"score_gpt":0.18659064944380496,"score_spread":0.17871678236412364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976529257","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":[0.9996779,0.00004550275,0.000024871346,0.000009764508,4.2982848e-7,0.0000015165963,0.000102470665,0.000003655274,0.00013380508],"genre_scores_gemma":[0.99963427,0.00003435397,0.0000679802,0.000006443049,5.4927915e-7,0.000001304404,0.00017891942,0.0000018452114,0.00007427764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99982846,0.000012109702,0.0000070751903,0.000041675386,0.000050776023,0.000059938197],"domain_scores_gemma":[0.99957114,0.00007215895,0.00007261195,0.000015709113,0.00014519515,0.0001231446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024431758,0.00024550382,0.00025531443,0.00054263294,0.0008771272,0.00058519933,0.00038273295,0.00022397046,0.00043797606],"category_scores_gemma":[0.0006513986,0.00021433667,0.00016148404,0.0004886099,0.000504055,0.00032652728,0.0002761292,0.00017619524,0.000051114384],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029533135,0.000060979663,0.9575187,0.000039365907,0.00010202457,0.00018561467,0.0003624196,0.001361517,0.031778507,0.000055280838,0.00020811998,0.008032105],"study_design_scores_gemma":[0.000001934605,0.000009576665,0.9990772,0.0000010799332,0.000006319234,0.000019817156,0.00006254026,0.0004894794,0.0002655117,0.0000056965687,0.000057779802,0.0000030817848],"about_ca_topic_score_codex":0.8883801,"about_ca_topic_score_gemma":0.9707366,"teacher_disagreement_score":0.11161989,"about_ca_system_score_codex":0.0049166935,"about_ca_system_score_gemma":0.0025931578,"threshold_uncertainty_score":0.22455442},"labels":[],"label_agreement":null},{"id":"W2073280230","doi":"10.2480/agrmet.59.117","title":"Mesh Climate Change Data for Evaluating Climate Change Impacts in Japan under Gradually Increasing Atmospheric CO2 Concentration","year":2003,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Climate variability and models","field":"Environmental Science","cited_by":49,"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":"Environmental science; Climatology; Precipitation; Latitude; Climate change; Longitude; Atmosphere (unit); Climate model; Atmospheric circulation; Atmospheric sciences; Anomaly (physics); Spatial distribution; Meteorology; Geography; Geology; Remote sensing","score_opus":0.10153033953276937,"score_gpt":0.3250120493210308,"score_spread":0.22348170978826143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073280230","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":[0.63310343,0.00035875474,0.0029934472,0.00022115944,0.00010845658,0.00020032558,0.35923737,0.000685948,0.0030911365],"genre_scores_gemma":[0.4260297,0.000236842,0.009355448,0.000059713057,0.00004450093,0.0005377087,0.56279033,0.00008059845,0.00086522434],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969006,0.00004070096,0.000047355945,0.00010890159,0.00006959917,0.000043412394],"domain_scores_gemma":[0.99906415,0.00010329258,0.00017747843,0.00023238614,0.00028512627,0.00013766179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037796656,0.00050817203,0.00034819887,0.0015311665,0.0004718027,0.0003714635,0.00075587764,0.0005120792,0.0015551271],"category_scores_gemma":[0.0011831648,0.00017989227,0.00053284527,0.0031706975,0.0002484318,0.0004956994,0.00060423755,0.00041319124,0.0004363475],"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":[0.0010320162,0.00085546466,0.6620024,0.0018859318,0.0007858177,0.0013040447,0.0010780692,0.102737606,0.014027722,0.0033687497,0.13708206,0.07384023],"study_design_scores_gemma":[0.00023298795,0.00012634578,0.8435208,0.0001138722,0.00025238172,0.00017887927,0.000970479,0.06944769,0.0055388645,0.00071879243,0.0787972,0.00010164191],"about_ca_topic_score_codex":0.045054905,"about_ca_topic_score_gemma":0.057305694,"teacher_disagreement_score":0.045054905,"about_ca_system_score_codex":0.0007412028,"about_ca_system_score_gemma":0.00091509346,"threshold_uncertainty_score":0.089585304},"labels":[],"label_agreement":null},{"id":"W2107208804","doi":"10.14877/agrmet2.sp09.0.157.0","title":"Prediction of soil-freezing depth in Tokachi Plain under global warming condition","year":2009,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Environmental science; Global warming; Hydrology (agriculture); Climatology; Geology; Soil science; Atmospheric sciences; Climate change; Geotechnical engineering; Oceanography","score_opus":0.03328233203933309,"score_gpt":0.24875348855477789,"score_spread":0.2154711565154448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107208804","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":[0.999278,0.000016400427,0.00031684045,0.000016453536,0.0000033730869,0.000003306587,0.00013649433,0.000016400301,0.00021280529],"genre_scores_gemma":[0.99964607,0.000009463549,0.00014798484,0.0000015652884,0.0000012564977,0.0000017871381,0.00013805127,0.0000010532326,0.000052823172],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999442,0.0000066107923,0.0000038654002,0.000018716391,0.0000061681976,0.000020491441],"domain_scores_gemma":[0.9997825,0.00006951837,0.00003149647,0.0000136920735,0.000051009032,0.000051819763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020918484,0.00034033819,0.00025251225,0.00051445543,0.00038483265,0.0005382731,0.00039859783,0.0005041776,0.0007515151],"category_scores_gemma":[0.0004059755,0.00022797873,0.00047495682,0.0004089639,0.0002793723,0.00041281857,0.0002166692,0.00027608656,0.00007806158],"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":[0.00046385443,0.00018214951,0.63709694,0.000041072362,0.00013300098,0.0006419672,0.00013749629,0.34843323,0.0070455996,0.00033628702,0.00035869624,0.005129756],"study_design_scores_gemma":[0.000048311813,0.00007726599,0.37226838,0.000005308674,0.000058410933,0.000048211292,0.00023215992,0.62534815,0.0015274386,0.0002333606,0.00013253574,0.000020446094],"about_ca_topic_score_codex":0.06700177,"about_ca_topic_score_gemma":0.05174072,"teacher_disagreement_score":0.06700177,"about_ca_system_score_codex":0.00076535245,"about_ca_system_score_gemma":0.0006979453,"threshold_uncertainty_score":0.13322353},"labels":[],"label_agreement":null},{"id":"W2193520858","doi":"10.14877/agrmet2.isam08.0.95.0","title":"Seasonal and Annual Water balance of Snow and Frozen Soil in Tokachi, Hokkaido, Japan","year":2008,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Snow; Environmental science; Water balance; Hydrology (agriculture); Physical geography; Climatology; Water equivalent; Geography; Geology; Meteorology; Geotechnical engineering","score_opus":0.015383703794357103,"score_gpt":0.20391194028025375,"score_spread":0.18852823648589664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193520858","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":[0.99939966,0.000054191194,0.00002508684,0.000016785696,0.0000038209128,0.0000021736612,0.0002816932,0.000002623364,0.0002140121],"genre_scores_gemma":[0.99915004,0.00004508528,0.000058490084,0.00001140118,0.000005595395,0.0000053843446,0.00042005428,0.0000016923506,0.00030218562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990225,0.0000075974826,0.000011424136,0.0000315608,0.000016424434,0.000030643914],"domain_scores_gemma":[0.99973756,0.00002384988,0.000046996913,0.000009921403,0.00009144546,0.00009028723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021390243,0.00030538213,0.00036394884,0.0009540047,0.0008099252,0.00043823564,0.00041510264,0.00036316467,0.00089373253],"category_scores_gemma":[0.00024464767,0.00026817585,0.0002746908,0.00095250795,0.00046149892,0.00056832586,0.00038711558,0.0002092389,0.00012344659],"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":[0.00034842314,0.000095323565,0.982786,0.00006282204,0.00022960767,0.0006439648,0.0012134882,0.0008169904,0.0093499245,0.000054152984,0.00047692974,0.003922429],"study_design_scores_gemma":[0.0000071897875,0.000017307562,0.99851996,0.0000018599361,0.00003574788,0.00004665521,0.0005037794,0.00046016308,0.00017399536,0.000011068233,0.00021813755,0.0000042388165],"about_ca_topic_score_codex":0.12561764,"about_ca_topic_score_gemma":0.24498357,"teacher_disagreement_score":0.12561764,"about_ca_system_score_codex":0.00095345185,"about_ca_system_score_gemma":0.00097235217,"threshold_uncertainty_score":0.2497729},"labels":[],"label_agreement":null},{"id":"W393363030","doi":"10.2480/agrmet.921","title":"Risk Assessment and Regionalization of Agro-meteorological Hazards in Jilin Province, China","year":2005,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Climate change impacts on agriculture","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":"Science North","funders":"","keywords":"China; Meteorological disasters; Agriculture; Environmental science; Temperate climate; Waterlogging (archaeology); Agricultural productivity; Natural hazard; Geography; Sustainable development; Distribution (mathematics); Global warming; Hazard; Climate change; Environmental protection; Meteorology; Ecology","score_opus":0.015917116647385973,"score_gpt":0.2631864376496006,"score_spread":0.2472693210022146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W393363030","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":[0.9945393,0.00018623563,0.004006791,0.00013228343,0.0000032916162,0.000049891856,0.00018956771,0.000022092681,0.00087046856],"genre_scores_gemma":[0.9985794,0.0000884833,0.0009895324,0.00000415207,0.0000018171478,0.00001248084,0.00015368844,0.0000013110374,0.00016913614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996575,0.00012938537,0.00002012756,0.000044079592,0.0000926441,0.0000562385],"domain_scores_gemma":[0.999161,0.0002627452,0.00022378699,0.000047521615,0.00023517023,0.000069819085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096952886,0.0003534049,0.0002169487,0.001519138,0.00027963318,0.00062320294,0.00034047675,0.00021613603,0.00040310767],"category_scores_gemma":[0.002222547,0.0001701686,0.00030238126,0.00082764565,0.00024522378,0.00030108963,0.00059723516,0.00014744836,0.000031444793],"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":[0.00023538058,0.00006536545,0.7317967,0.00008301029,0.00020748406,0.00068895926,0.00048231235,0.23103887,0.0024201425,0.0021356044,0.0006568815,0.030189246],"study_design_scores_gemma":[0.00003664839,0.00013788669,0.5750342,0.00002607583,0.00012337424,0.00020868324,0.0010191397,0.4185915,0.0010423332,0.0022926533,0.0014607979,0.000026710983],"about_ca_topic_score_codex":0.05480678,"about_ca_topic_score_gemma":0.049653165,"teacher_disagreement_score":0.05480678,"about_ca_system_score_codex":0.0015004305,"about_ca_system_score_gemma":0.0011194603,"threshold_uncertainty_score":0.10897553},"labels":[],"label_agreement":null},{"id":"W409994593","doi":"10.2480/agrmet.59.227","title":"Estimates of Snowfall Depth, Maximum Snow Depth, and Snow Pack Environments under Global Warming in Japan from Five Sets of Predicted Data","year":2003,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":34,"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":"Snow; Precipitation; Environmental science; Climatology; Global warming; Climate change; Snow cover; Physical geography; Atmospheric sciences; Meteorology; Geography; Geology; Oceanography","score_opus":0.028131040499486122,"score_gpt":0.24195345652738748,"score_spread":0.21382241602790136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W409994593","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":[0.99671614,0.00012623495,0.0017641621,0.000017705433,0.0000048647607,0.000006220285,0.00097009947,0.00004151136,0.000353165],"genre_scores_gemma":[0.996271,0.00007048494,0.0014672898,0.0000044202984,0.000003396065,0.000011380955,0.002091851,0.0000035512678,0.00007657637],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997938,0.000037360947,0.000030816067,0.00005936079,0.000047824633,0.000030767027],"domain_scores_gemma":[0.999629,0.000072572184,0.0000867223,0.000037387435,0.0001323851,0.00004187669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004922921,0.0005738859,0.0002217838,0.0010215198,0.0003054217,0.00037842154,0.00022775366,0.00023149629,0.0003075152],"category_scores_gemma":[0.00081609737,0.0002778203,0.00075503025,0.0006857341,0.00016531038,0.00041685457,0.00035046163,0.00014416485,0.000079389516],"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":[0.00012263561,0.00003493598,0.93770903,0.00009112627,0.00029486782,0.00013744377,0.00025314663,0.041719064,0.0023213343,0.00011457701,0.00039470263,0.016807154],"study_design_scores_gemma":[0.00001621951,0.00005311193,0.9430046,0.000014074254,0.00013883902,0.00006405324,0.00027721966,0.054286245,0.0012447119,0.00014734585,0.0007291788,0.000024429728],"about_ca_topic_score_codex":0.028423183,"about_ca_topic_score_gemma":0.03429781,"teacher_disagreement_score":0.028423183,"about_ca_system_score_codex":0.00057727273,"about_ca_system_score_gemma":0.0004219082,"threshold_uncertainty_score":0.056515515},"labels":[],"label_agreement":null},{"id":"W4390771174","doi":"10.2480/agrmet.d-23-00014","title":"Comparisons of different sample air-drying systems for carbon dioxide flux measurements based on eddy covariance in cold environments","year":2024,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Wind and Air Flow Studies","field":"Environmental Science","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 Calgary","funders":"","keywords":"Eddy covariance; Carbon dioxide; Flux (metallurgy); Sample (material); Environmental science; Atmospheric sciences; Carbon dioxide in Earth's atmosphere; Meteorology; Covariance; Materials science; Physics; Thermodynamics; Statistics; Mathematics; Chemistry; Ecosystem","score_opus":0.028449051262932335,"score_gpt":0.24093002766378818,"score_spread":0.21248097640085584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390771174","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":[0.98160535,0.0007382959,0.01673904,0.000021849204,0.000036053527,0.00012333805,0.00025758322,0.00007600564,0.00040244308],"genre_scores_gemma":[0.95695597,0.00081597245,0.040584963,0.000050318926,0.00001903318,0.000268806,0.0006974834,0.000079545636,0.00052795553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994361,0.00014256594,0.00007843068,0.000115634364,0.00019029336,0.000037056547],"domain_scores_gemma":[0.99911445,0.00032826464,0.00014624324,0.00008819869,0.0002788104,0.00004396671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016311574,0.00042307275,0.0004622893,0.0005589302,0.00057901844,0.00054725964,0.00032585338,0.00046397524,0.00035376946],"category_scores_gemma":[0.0019556372,0.0002909642,0.00042667476,0.00045943505,0.00036345545,0.00070538203,0.0003261275,0.00034247836,0.00012416295],"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":[0.0005145324,0.00013577969,0.017139576,0.00026926692,0.000083134015,0.00003649007,0.00021372105,0.0004613228,0.966895,0.00011673928,0.00009429424,0.014040143],"study_design_scores_gemma":[0.00003663574,0.0008661572,0.1863939,0.000029621513,0.00021764776,0.00015055401,0.00019595615,0.0052136225,0.8051833,0.00006507476,0.0016078665,0.0000395633],"about_ca_topic_score_codex":0.0015520008,"about_ca_topic_score_gemma":0.005042722,"teacher_disagreement_score":0.0016311574,"about_ca_system_score_codex":0.00024015672,"about_ca_system_score_gemma":0.00019349044,"threshold_uncertainty_score":0.008626461},"labels":[],"label_agreement":null},{"id":"W4408931974","doi":"10.2480/agrmet.d-24-00033","title":"Toward improving global rice yield reference dataset compilation through machine learning: Insights from training data selection and random forest analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; University of British Columbia; U.S. Geological Survey; University of Minnesota; National Aeronautics and Space Administration","keywords":"Random forest; Selection (genetic algorithm); Computer science; Machine learning; Artificial intelligence; Yield (engineering); Training (meteorology); Training set; Geography; Meteorology","score_opus":0.06292614207723515,"score_gpt":0.2690514841701182,"score_spread":0.20612534209288305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408931974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16541448,0.0016994689,0.8243328,0.00088648277,0.00008385774,0.00014225813,0.0028652786,0.0025533638,0.0020219702],"genre_scores_gemma":[0.44113317,0.0009896012,0.5452081,0.00021069021,0.00009559979,0.00023937025,0.01125574,0.00036875668,0.00049892324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983236,0.00084642495,0.00013073334,0.00036750792,0.00025350114,0.000078211626],"domain_scores_gemma":[0.99565035,0.0020501236,0.0003726785,0.0008851388,0.0009757135,0.00006600758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008793777,0.0011320567,0.0008736985,0.0023804484,0.00032906173,0.0010998228,0.0010142708,0.00062246335,0.00043756337],"category_scores_gemma":[0.0147794625,0.00022940127,0.0008551723,0.0029252684,0.0004180223,0.0015577764,0.0012698664,0.0007947319,0.0003293903],"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":[0.00019960932,0.00027944913,0.087958366,0.00059381983,0.00045254355,0.0003341439,0.00042096342,0.35119772,0.013281495,0.009161575,0.010186349,0.525934],"study_design_scores_gemma":[0.000047023652,0.00013347156,0.04258438,0.00014588915,0.00012973695,0.00009820714,0.000298826,0.92310166,0.010530359,0.011286937,0.011573523,0.000070057686],"about_ca_topic_score_codex":0.005407464,"about_ca_topic_score_gemma":0.007249317,"teacher_disagreement_score":0.008793777,"about_ca_system_score_codex":0.00041892464,"about_ca_system_score_gemma":0.0010819719,"threshold_uncertainty_score":0.046506464},"labels":[],"label_agreement":null}]}