{"meta":{"query_hash":"7bf3daa118de","filters":{"venue":"Journal of Environmental & Earth Sciences"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/7bf3daa118de","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Environmental+%26+Earth+Sciences"},"results":[{"id":"W4283013738","doi":"10.30564/jees.v4i2.4687","title":"Vegetation Changes in Alberta Oil Sands, Canada, Based on Remotely Sensed Data from 1995 to 2020","year":2022,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":2,"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":"Vegetation (pathology); Tailings; Normalized Difference Vegetation Index; Oil sands; Environmental science; Sand mining; Vegetation cover; Land use; Physical geography; Land cover; Scale (ratio); Hydrology (agriculture); Mining engineering; Remote sensing; Geography; Geology; Climate change; Archaeology; Cartography; Civil engineering; Geotechnical engineering; Engineering","score_opus":0.018303958587415972,"score_gpt":0.21170370451378587,"score_spread":0.19339974592636988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283013738","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":[0.97275203,0.0010303553,0.00016430843,0.00017817506,0.000016177779,0.000021877473,0.021806402,0.000043745287,0.003986849],"genre_scores_gemma":[0.97861665,0.0008984954,0.00064377696,0.00006874496,0.0000044911767,0.000012533435,0.016502677,0.0000076249435,0.0032450669],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997948,0.000007480432,0.000010962656,0.000034747136,0.000099016535,0.000052945932],"domain_scores_gemma":[0.9995365,0.000019227808,0.000050559152,0.000010065408,0.0003150243,0.00006868055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024923132,0.00032034135,0.00016724959,0.0019990155,0.00085443223,0.0009226661,0.00045186057,0.00021536442,0.00087386795],"category_scores_gemma":[0.0006032285,0.0001407417,0.00026527682,0.0038262683,0.00029879622,0.00021477343,0.0003048024,0.00024690825,0.00021067043],"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":[0.00037803221,0.00009460779,0.9457859,0.00017368856,0.0001392525,0.00042601756,0.0007633465,0.0038557008,0.0028219619,0.0003508167,0.008793328,0.03641747],"study_design_scores_gemma":[0.0000049316227,0.0000066730263,0.9949588,0.000019452971,0.000019051917,0.000036509573,0.0006071938,0.0014578796,0.0002241588,0.000014855572,0.0026424672,0.000008132034],"about_ca_topic_score_codex":0.9941825,"about_ca_topic_score_gemma":0.9977943,"teacher_disagreement_score":0.017868042,"about_ca_system_score_codex":0.017868042,"about_ca_system_score_gemma":0.011344994,"threshold_uncertainty_score":0.12964231},"labels":[],"label_agreement":null},{"id":"W4403286946","doi":"10.30564/jees.v6i3.6962","title":"A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Advanced Technologies in Various Fields","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Adaptation (eye); Internet of Things; Computer science; Ecosystem; The Internet; Data science; Artificial intelligence; World Wide Web; Ecology; Psychology; Biology; Neuroscience","score_opus":0.024629710000786603,"score_gpt":0.2882659874282083,"score_spread":0.2636362774274217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403286946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026805212,0.00045809118,0.9911327,0.0011254108,0.000054135162,0.00039178916,0.00025997742,0.0005984338,0.0032989655],"genre_scores_gemma":[0.094910376,0.0006369889,0.9018363,0.00017619952,0.00006193242,0.00084136036,0.00046234176,0.000050851795,0.001023565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625,0.0015458431,0.0003762817,0.00076364324,0.0008568567,0.0002073889],"domain_scores_gemma":[0.9967451,0.00150619,0.00048341358,0.00036270442,0.0006732069,0.00022939469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006433117,0.002150935,0.0007801622,0.005939954,0.001202631,0.003887343,0.003177325,0.0020687347,0.0015590547],"category_scores_gemma":[0.005541321,0.00076972856,0.0025468657,0.0024212913,0.002914931,0.004439654,0.0034167771,0.0018934646,0.00045225676],"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":[0.00006847049,0.00042853702,0.008520221,0.000892017,0.00035057572,0.0007557011,0.0010464626,0.31760338,0.0047816928,0.5476809,0.005801574,0.112070434],"study_design_scores_gemma":[0.000032340806,0.00019192464,0.0028668165,0.00044223445,0.00012208878,0.0003285099,0.0006583338,0.74060345,0.002329452,0.21593894,0.03635113,0.00013473665],"about_ca_topic_score_codex":0.015721887,"about_ca_topic_score_gemma":0.013229462,"teacher_disagreement_score":0.015721887,"about_ca_system_score_codex":0.0029513245,"about_ca_system_score_gemma":0.004069112,"threshold_uncertainty_score":0.034021974},"labels":[],"label_agreement":null},{"id":"W4406808734","doi":"10.30564/jees.v7i2.7533","title":"Mapping Hotspots and Emerging Trends in Global Wetlands Research: A Scientometric Analysis (2002–2022)","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":4,"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":"Universiti Putra Malaysia","keywords":"Wetland; Geography; Environmental science; Data science; Computer science; Ecology; Biology","score_opus":0.03578703982893041,"score_gpt":0.31041163724790644,"score_spread":0.27462459741897605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406808734","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":[0.7998042,0.046769876,0.0057280073,0.0034814824,0.0003994197,0.0009560737,0.1231512,0.00047354837,0.019236194],"genre_scores_gemma":[0.8854737,0.02856057,0.010258922,0.00028469122,0.0005738843,0.001606809,0.07091789,0.0001314599,0.0021920395],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.98733264,0.0026177205,0.0032415632,0.0011579691,0.004797624,0.0008524441],"domain_scores_gemma":[0.94939077,0.026947087,0.010018961,0.0024128142,0.009982556,0.0012478522],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.013838918,0.0009992551,0.0016185096,0.16306365,0.0013100728,0.005825066,0.00068132824,0.00071132794,0.0027299176],"category_scores_gemma":[0.046214215,0.00034073408,0.0027944006,0.23744048,0.00082539866,0.004258645,0.0036408582,0.0006619112,0.000759723],"study_design_candidate":"not_applicable","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":[0.00045980763,0.00014216891,0.6230752,0.016050054,0.003031053,0.0013070705,0.007651846,0.003758267,0.0030339914,0.0056343153,0.030440714,0.30541554],"study_design_scores_gemma":[0.000039070666,0.00017824466,0.91213596,0.001636669,0.00095187157,0.00085344823,0.007722243,0.0035596394,0.0012008502,0.0024312884,0.069199406,0.000091258305],"about_ca_topic_score_codex":0.0066082207,"about_ca_topic_score_gemma":0.0069394433,"teacher_disagreement_score":0.83693635,"about_ca_system_score_codex":0.002209833,"about_ca_system_score_gemma":0.003021893,"threshold_uncertainty_score":0.07318807},"labels":[],"label_agreement":null},{"id":"W4408552976","doi":"10.30564/jees.v7i4.7811","title":"The Role of Pigs in the Carbon Footprint of Red Meat in Canada","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Agriculture Sustainability and Environmental Impact","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":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Carbon footprint; Red meat; Footprint; Ecological footprint; Environmental science; Geography; Greenhouse gas; Food science; Biology; Ecology; Archaeology; Sustainability","score_opus":0.003207230762901299,"score_gpt":0.18905263060639185,"score_spread":0.18584539984349055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408552976","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":[0.9432329,0.008329102,0.006490826,0.0036396575,0.00012028057,0.00007850152,0.017278474,0.00023093155,0.020599332],"genre_scores_gemma":[0.9903037,0.0019242689,0.0020527102,0.00029892256,0.000009248562,0.000021922922,0.0022752187,0.000039972365,0.0030741198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99968636,0.00007909737,0.00000999945,0.00006767157,0.000050646264,0.000106201005],"domain_scores_gemma":[0.99936885,0.00017222938,0.000042985015,0.000033238808,0.0002857117,0.0000970378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009355039,0.00065752957,0.00085487816,0.0009249521,0.0013318877,0.0021354205,0.0016145271,0.00071382493,0.0026658042],"category_scores_gemma":[0.0014378657,0.0005905292,0.0016387625,0.0019343254,0.0007922892,0.00075888843,0.0007518733,0.0006954746,0.00014119901],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036476427,0.000044023294,0.068638615,0.00023816706,0.0010303413,0.00028342978,0.00011520192,0.9078491,0.00076733704,0.007405981,0.004234351,0.009028726],"study_design_scores_gemma":[0.00024409746,0.000113075905,0.07807232,0.00029755782,0.001495463,0.000103912425,0.00059194444,0.89812934,0.0008621197,0.0058416,0.014071504,0.00017711078],"about_ca_topic_score_codex":0.98618096,"about_ca_topic_score_gemma":0.9820911,"teacher_disagreement_score":0.03228229,"about_ca_system_score_codex":0.03228229,"about_ca_system_score_gemma":0.024915803,"threshold_uncertainty_score":0.23422551},"labels":[],"label_agreement":null},{"id":"W4411570452","doi":"10.30564/jees.v7i6.9315","title":"Detecting Plastic Pollution in Aquatic Environment Using Remote Sensing Technology: Cost-Saving Method in Pollution and Risk Management for Developing Countries","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Microplastics and Plastic Pollution","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 New Brunswick","funders":"Central University of Technology","keywords":"Pollution; Environmental science; Environmental planning; Developing country; Environmental resource management; Risk analysis (engineering); Environmental protection; Business; Environmental engineering; Economic growth; Economics; Ecology","score_opus":0.012051870755444415,"score_gpt":0.255683057718973,"score_spread":0.24363118696352856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411570452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23022974,0.17848572,0.51098734,0.012938616,0.0011960119,0.0008699973,0.002460043,0.0011263762,0.061706215],"genre_scores_gemma":[0.56026584,0.10306523,0.32215616,0.0008695231,0.00045794938,0.0004572581,0.0009807324,0.000094146206,0.011653112],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992636,0.00019324616,0.00004280051,0.00008992775,0.0003698938,0.00004060333],"domain_scores_gemma":[0.9994722,0.00014920351,0.00011288644,0.00003920758,0.00020240263,0.000024164214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091103464,0.0006601385,0.0005328427,0.0022999856,0.0003918236,0.0011914462,0.0006586343,0.00077441,0.002507125],"category_scores_gemma":[0.00086032745,0.00026334726,0.00058707193,0.0027440835,0.000357085,0.0014336236,0.0007770416,0.00060196326,0.0008089376],"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":[0.00018803363,0.00019778598,0.028935513,0.0030538063,0.00013000758,0.0010229341,0.00032943606,0.006361014,0.09776205,0.006892148,0.0073886546,0.8477387],"study_design_scores_gemma":[0.00011399079,0.0018193559,0.1480782,0.0033368762,0.0012627001,0.0068662586,0.0068240613,0.084684044,0.30555397,0.03909725,0.40171188,0.00065141456],"about_ca_topic_score_codex":0.002090364,"about_ca_topic_score_gemma":0.0035098905,"teacher_disagreement_score":0.002507125,"about_ca_system_score_codex":0.00051629887,"about_ca_system_score_gemma":0.0009799537,"threshold_uncertainty_score":0.008387148},"labels":[],"label_agreement":null},{"id":"W4411574169","doi":"10.30564/jees.v7i7.7676","title":"Assessing the Convergence of Cropland Ecological Balance: A Panel Data Analysis of 13 Major Agricultural Countries","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Sustainable Agricultural Systems Analysis","field":"Environmental Science","cited_by":3,"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":"Agriculture; Convergence (economics); Balance (ability); Panel data; Balance of nature; Environmental science; Natural resource economics; Ecology; Economics; Geography; Econometrics; Biology; Economic growth","score_opus":0.018003867268309933,"score_gpt":0.2703127927959178,"score_spread":0.25230892552760786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411574169","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":[0.9948009,0.00011555815,0.0019931244,0.000080115045,0.000005711807,0.000016055881,0.0021551615,0.000013343369,0.0008200812],"genre_scores_gemma":[0.993305,0.0000819087,0.0009676045,0.000026883235,0.0000044827957,0.000022981078,0.0053736465,0.000004402098,0.0002129652],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99899167,0.00049221015,0.00006273764,0.0002159697,0.000103449216,0.00013391497],"domain_scores_gemma":[0.9969746,0.0015310809,0.00072054786,0.0002963811,0.0003152049,0.00016219789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020058947,0.00026365076,0.00048315243,0.0011214344,0.0003912747,0.0008987491,0.0004057619,0.0005054717,0.0012468938],"category_scores_gemma":[0.0038094167,0.0001965245,0.0009573761,0.0020499385,0.00032238854,0.00065316283,0.00078703044,0.000766066,0.00027175585],"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":[0.000157782,0.00012187088,0.95993423,0.00004481657,0.0005269688,0.00028830513,0.00031086206,0.027151981,0.0004186798,0.00075822877,0.0012143878,0.009071808],"study_design_scores_gemma":[0.000017355467,0.00016717808,0.9480064,0.000033819546,0.00018194283,0.00013494615,0.0018998488,0.045175012,0.00069162535,0.0008748398,0.002781634,0.000035344023],"about_ca_topic_score_codex":0.021241577,"about_ca_topic_score_gemma":0.016205912,"teacher_disagreement_score":0.021241577,"about_ca_system_score_codex":0.00056804204,"about_ca_system_score_gemma":0.00046872866,"threshold_uncertainty_score":0.04223585},"labels":[],"label_agreement":null},{"id":"W4413304972","doi":"10.30564/jees.v7i8.10047","title":"Enhancing Smallholder Aquaculture in Philippine Peatlands: Challenges, Opportunities, and Nature-Based Solutions in the Leyte Sab-a Basin","year":2025,"lang":"en","type":"article","venue":"Journal of Environmental & Earth Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"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":"International Development Research Centre; Government of Canada","keywords":"Peat; Aquaculture; Structural basin; Agroforestry; Environmental science; Geography; Fishery; Ecology; Geology; Fish <Actinopterygii>; Biology; Geomorphology","score_opus":0.028189214878267716,"score_gpt":0.23746765398776967,"score_spread":0.20927843910950195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413304972","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":[0.9679354,0.010846131,0.0019182131,0.0053424845,0.0000460453,0.00012560222,0.00008626079,0.000030189327,0.0136697],"genre_scores_gemma":[0.9919974,0.005155679,0.0014391827,0.00027277129,0.000009493888,0.000054884684,0.000027969389,0.000003721549,0.0010388762],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995907,0.00018136116,0.000021372125,0.00003424009,0.00007355078,0.000098910925],"domain_scores_gemma":[0.99895084,0.0004545329,0.0002099282,0.000020937905,0.0001375572,0.00022621262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013048763,0.00030604782,0.00013801001,0.00079721404,0.0012907819,0.0017569971,0.0006387749,0.0005285585,0.0014014697],"category_scores_gemma":[0.0011934283,0.000104769126,0.00012245019,0.00097167055,0.0011528917,0.001579667,0.0017521782,0.00046425508,0.00009230496],"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":[0.00024487905,0.0005807317,0.15573727,0.014310479,0.0001536148,0.039115317,0.12129031,0.004820601,0.03887966,0.027583517,0.0051720976,0.59211147],"study_design_scores_gemma":[0.000039151164,0.000976196,0.23356418,0.0046541095,0.00014234736,0.0037520044,0.5551528,0.0040054503,0.007868782,0.0116565805,0.17807865,0.00010982477],"about_ca_topic_score_codex":0.009399723,"about_ca_topic_score_gemma":0.031046808,"teacher_disagreement_score":0.009399723,"about_ca_system_score_codex":0.0018756668,"about_ca_system_score_gemma":0.006498295,"threshold_uncertainty_score":0.01868999},"labels":[],"label_agreement":null}]}