{"meta":{"query_hash":"4129c7ffb60f","filters":{"venue":"Journal of Geography and Regional Planning"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/4129c7ffb60f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Geography+and+Regional+Planning"},"results":[{"id":"W2111718048","doi":"10.5897/jgrp.9000056","title":"Finding the growth rate during formation of South American community of Nations","year":2009,"lang":"en","type":"article","venue":"Journal of Geography and Regional Planning","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","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":"Latin Americans; Geography; Value (mathematics); Economic integration; Development economics; Economic geography; Economics; International trade; International economics; Political science; Statistics; Mathematics","score_opus":0.036560632509226886,"score_gpt":0.22832630834198686,"score_spread":0.19176567583275997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111718048","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":[0.9499563,0.00031141,0.03815743,0.00024331425,0.000034692635,0.0000481779,0.0010218875,0.00009980856,0.010127064],"genre_scores_gemma":[0.9787676,0.00029432398,0.01858335,0.000007289445,0.00001013172,0.000045835855,0.0011547825,0.000030883934,0.0011057288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99946016,0.00013249725,0.000028103606,0.00013168146,0.00016761653,0.00007986526],"domain_scores_gemma":[0.9974178,0.00093142997,0.00038977514,0.00025988035,0.00088106917,0.000120046774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012511434,0.0001843817,0.00019117867,0.0022444949,0.0005834286,0.0010888651,0.00030853186,0.00023597128,0.001604824],"category_scores_gemma":[0.009224959,0.000103946746,0.00031681635,0.0020796144,0.0002544743,0.00097411533,0.00071872666,0.00048095462,0.00022106769],"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":[0.00022733682,0.00005896839,0.67062616,0.0001391515,0.00009887363,0.0004838741,0.0026217576,0.049689956,0.0046388274,0.05682467,0.0027654094,0.21182497],"study_design_scores_gemma":[0.000021877157,0.00015885574,0.51461345,0.00014317258,0.00006524415,0.00084622164,0.0064009433,0.3976831,0.014598107,0.030399641,0.03494931,0.000120024444],"about_ca_topic_score_codex":0.017631609,"about_ca_topic_score_gemma":0.013103038,"teacher_disagreement_score":0.017631609,"about_ca_system_score_codex":0.000784331,"about_ca_system_score_gemma":0.0008701104,"threshold_uncertainty_score":0.035057962},"labels":[],"label_agreement":null},{"id":"W2965516077","doi":"10.5897/jgrp.9000034","title":"Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer","year":2007,"lang":"en","type":"article","venue":"Journal of Geography and Regional Planning","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":28,"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":"Thematic map; Land cover; Pixel; Object (grammar); Geography; Remote sensing; Artificial intelligence; Segmentation; Cartography; Object based; Contextual image classification; Computer science; Cover (algebra); Pattern recognition (psychology); Computer vision; Image (mathematics); Land use; Engineering","score_opus":0.08041880365613424,"score_gpt":0.3720888075945139,"score_spread":0.29167000393837966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965516077","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7348032,0.0007323506,0.24499774,0.00022646008,0.00008852586,0.00029394912,0.0012346614,0.0022341984,0.015388802],"genre_scores_gemma":[0.77438504,0.00046571784,0.21941832,0.00007685982,0.000020475623,0.00010098553,0.0012303424,0.00013974612,0.0041624987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991013,0.000206286,0.0000541991,0.00011005173,0.0004742825,0.000053907894],"domain_scores_gemma":[0.99780697,0.00071642443,0.0001213412,0.00014044195,0.0011738227,0.00004111818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019274307,0.000425362,0.00029349566,0.0022349686,0.00027258205,0.0008271355,0.00054627156,0.00034206445,0.0013396076],"category_scores_gemma":[0.0035244448,0.00018665829,0.00030306814,0.0014703437,0.0002404046,0.0006621387,0.00029329286,0.00018424515,0.00066925224],"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":[0.00081223674,0.00022863415,0.08482889,0.0005278913,0.00035446405,0.00016903649,0.0008374846,0.018894507,0.0490186,0.0014250599,0.0030384318,0.8398647],"study_design_scores_gemma":[0.00016140439,0.0005426684,0.34420362,0.00013260401,0.00054182543,0.0009328479,0.0016520544,0.5573298,0.07983908,0.0014702766,0.013021848,0.00017200883],"about_ca_topic_score_codex":0.021016445,"about_ca_topic_score_gemma":0.03552166,"teacher_disagreement_score":0.021016445,"about_ca_system_score_codex":0.0007070171,"about_ca_system_score_gemma":0.0004352406,"threshold_uncertainty_score":0.04178822},"labels":[],"label_agreement":null},{"id":"W3194687880","doi":"10.5897/jgrp2021.0829","title":"Why populations are not planets_ gravity and the limits of disease modeling by analogy","year":2021,"lang":"en","type":"article","venue":"Journal of Geography and Regional Planning","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","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 British Columbia","funders":"","keywords":"Planet; Gravity model of trade; Population; Geography; Disease; Analogy; Econometrics; Demography; Mathematics; Medicine; Astrophysics; Physics; Economics; Pathology; Sociology","score_opus":0.05119520899645052,"score_gpt":0.31198703901817926,"score_spread":0.26079183002172873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194687880","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":[0.21079728,0.009625309,0.58054584,0.11202907,0.0009367416,0.000099799574,0.000937162,0.000850342,0.084178366],"genre_scores_gemma":[0.9479677,0.0027475632,0.03946164,0.0022523312,0.0006662823,0.00013091449,0.00019627306,0.00018463768,0.0063925534],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970425,0.0019095913,0.00010710565,0.0005350935,0.00024051817,0.00016523038],"domain_scores_gemma":[0.9850129,0.0117510855,0.00094435,0.0013190226,0.0005606428,0.000412003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056809895,0.00045806722,0.0012354095,0.0012586712,0.0011364591,0.0037461147,0.0019197499,0.0018254989,0.0050353874],"category_scores_gemma":[0.031575587,0.00045614672,0.0014425651,0.0012407162,0.0061182184,0.00742908,0.002641084,0.0031855726,0.00080122764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029286497,0.00003546354,0.009290076,0.00008544908,0.00008079023,0.0001168833,0.0013117483,0.046974633,0.00015810358,0.9210729,0.0047232606,0.016121253],"study_design_scores_gemma":[0.000010597022,0.00001856334,0.0018137298,0.000046336332,0.000017189775,0.00009925154,0.00022320205,0.08513819,0.000050018876,0.90589523,0.0066678007,0.00001993391],"about_ca_topic_score_codex":0.017249275,"about_ca_topic_score_gemma":0.0062219496,"teacher_disagreement_score":0.017249275,"about_ca_system_score_codex":0.0017871863,"about_ca_system_score_gemma":0.0009171439,"threshold_uncertainty_score":0.034297764},"labels":[],"label_agreement":null}]}