{"meta":{"query_hash":"94f853732106","filters":{"venue":"Geodata and AI."},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/94f853732106","api":"https://metacan.xera.ac/api/v1/cohort?venue=Geodata+and+AI."},"results":[{"id":"W4408300736","doi":"10.1016/j.geoai.2025.100014","title":"A framework to optimize a designed geotechnical system probabilistically using MLP-ANN and ELECTERE decision making – a nailed wall study","year":2025,"lang":"en","type":"article","venue":"Geodata and AI.","topic":"Geotechnical Engineering and Analysis","field":"Engineering","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":"Rocscience (Canada)","funders":"","keywords":"Computer science; Geotechnical engineering; Engineering; Civil engineering; Artificial intelligence; Machine learning; Construction engineering; Structural engineering","score_opus":0.00988349175852647,"score_gpt":0.2677609214679615,"score_spread":0.257877429709435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408300736","genre_codex":"methods","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":[0.32462063,0.0002552015,0.67435324,0.00006713438,0.000048911712,0.00029820218,0.00000702667,0.00033060595,0.000019040297],"genre_scores_gemma":[0.93133116,0.000014238345,0.0684662,0.00008493687,0.000025511867,0.000038831404,0.000003821494,0.000023219603,0.000012082076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986609,0.000037413014,0.00035858504,0.00044273958,0.00016083801,0.0003394933],"domain_scores_gemma":[0.9990914,0.00024380113,0.000018440644,0.00045303805,0.00005883951,0.00013450866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030518064,0.0002335944,0.00043302504,0.00017655035,0.000115359064,0.00018988042,0.00019126124,0.00018332287,0.000011180219],"category_scores_gemma":[0.0004206892,0.00021466282,0.000052539115,0.0005871308,0.000023323402,0.00007398269,0.00024280936,0.00032324382,0.0000037637276],"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":[0.00011419574,0.0001749828,0.00050808344,0.00066733506,0.0004250603,0.00014611214,0.0002234557,0.963589,0.0032882297,0.002220247,0.00026337607,0.02837993],"study_design_scores_gemma":[0.00038652154,0.000091722206,0.001558473,0.0012754896,0.00025855246,0.000025712783,0.00016411213,0.9952255,0.000032943786,0.00042026403,0.00024104232,0.00031969498],"about_ca_topic_score_codex":0.000019711935,"about_ca_topic_score_gemma":0.000006167152,"teacher_disagreement_score":0.6067105,"about_ca_system_score_codex":0.00006750735,"about_ca_system_score_gemma":0.00002256516,"threshold_uncertainty_score":0.8753696},"labels":[],"label_agreement":null},{"id":"W4408815459","doi":"10.1016/j.geoai.2025.100015","title":"Book review databases for data-centric geotechnics: Geotechnical structures, edited by Chong Tang and Kok-Kwang Phoon, published by Routledge, 2025, ISBN: 9781032578958","year":2025,"lang":"en","type":"article","venue":"Geodata and AI.","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rocscience (Canada)","funders":"","keywords":"Philosophy","score_opus":0.009132190895185539,"score_gpt":0.2524941425672839,"score_spread":0.24336195167209837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408815459","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012606855,0.7697524,0.21893875,0.004814119,0.00029903915,0.0006153816,0.0047016465,0.00068786414,0.00006476662],"genre_scores_gemma":[0.019839939,0.83542275,0.0075139017,0.02838317,0.0010703899,0.00057036977,0.097273305,0.00038329526,0.009542874],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99768406,0.000031844324,0.0006014051,0.0008672763,0.00023498877,0.00058040716],"domain_scores_gemma":[0.9979527,0.00017974887,0.00007014779,0.001494935,0.00008575793,0.0002166931],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045518324,0.00042131034,0.000723455,0.00018008865,0.0001644507,0.00023659089,0.0007071539,0.0002356075,0.00021623482],"category_scores_gemma":[0.0006161848,0.0003984279,0.00008970174,0.0007398891,0.0000842578,0.0007905063,0.0006771192,0.0005872761,0.0000036282838],"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":[0.0000043286027,0.000025820182,0.0000067429905,0.0014651197,0.00012800707,0.00000427471,0.0000012876898,0.00047222516,0.0006786694,0.0001110961,0.98464006,0.012462384],"study_design_scores_gemma":[0.0004871491,0.000012701833,0.000021352665,0.00071910967,0.0003373334,0.00001233677,0.000006216238,0.10500386,0.00033163725,0.000051681225,0.8926318,0.00038480386],"about_ca_topic_score_codex":0.00007574162,"about_ca_topic_score_gemma":0.000004995542,"teacher_disagreement_score":0.21142486,"about_ca_system_score_codex":0.00004059517,"about_ca_system_score_gemma":0.00003686629,"threshold_uncertainty_score":0.99984676},"labels":[],"label_agreement":null},{"id":"W4411227373","doi":"10.1016/j.geoai.2025.100026","title":"Evaluating Natural Language Processing Algorithms for Improved Hazard and Operability Analysis","year":2025,"lang":"en","type":"article","venue":"Geodata and AI.","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Operability; Computer science; Hazard; Algorithm; Natural (archaeology); Hazard analysis; Hazard and operability study; Reliability engineering; Engineering; Software engineering; Chemistry; Geology","score_opus":0.0898132510973513,"score_gpt":0.48291427612880483,"score_spread":0.3931010250314535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411227373","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":[0.88905317,0.011948443,0.09041175,0.0077003743,0.00012461675,0.0002965982,0.00020820508,0.000032643064,0.00022419376],"genre_scores_gemma":[0.9815086,0.0000841344,0.015547754,0.0005650258,0.000039624298,0.000013783354,0.00007094945,0.0000032667735,0.0021668402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838805,0.00011924663,0.0004008045,0.0006249031,0.0002751279,0.00019187735],"domain_scores_gemma":[0.9985346,0.00053835026,0.00009401607,0.00045071557,0.00032268348,0.000059617243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028267393,0.000111623296,0.0003920093,0.00023077958,0.00039680372,0.0005638922,0.0002620812,0.000053409844,0.000051742863],"category_scores_gemma":[0.0022530959,0.00007672834,0.00013230731,0.0012926772,0.00008964574,0.00036964644,0.00022356454,0.00010266476,0.0000010502301],"study_design_candidate":"design_other","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":[0.000024379106,0.000014974194,0.007885688,0.000014447281,0.00014162183,7.088241e-7,0.0003893107,0.000071510905,0.0010901935,0.00004112029,0.00023740232,0.99008864],"study_design_scores_gemma":[0.00042082393,0.00002073789,0.026465282,0.00001161362,0.00055816106,0.0000010763226,0.0023575488,0.96465206,0.00029823527,0.0036066177,0.0014815651,0.00012630003],"about_ca_topic_score_codex":0.00012873505,"about_ca_topic_score_gemma":0.00043320426,"teacher_disagreement_score":0.98996234,"about_ca_system_score_codex":0.000010882809,"about_ca_system_score_gemma":0.00007876602,"threshold_uncertainty_score":0.5437625},"labels":[],"label_agreement":null},{"id":"W4412564562","doi":"10.1016/j.geoai.2025.100030","title":"A hybrid deep learning-Bayesian optimization model for enhanced slope stability classification","year":2025,"lang":"en","type":"article","venue":"Geodata and AI.","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Stability (learning theory); Artificial intelligence; Bayesian optimization; Bayesian probability; Computer science; Deep learning; Machine learning","score_opus":0.011122044864166788,"score_gpt":0.23985359062252518,"score_spread":0.22873154575835838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412564562","genre_codex":"methods","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":[0.0419588,0.00005443286,0.9535144,0.00061936275,0.000043006592,0.00018248422,0.000014716303,0.000026230255,0.0035865689],"genre_scores_gemma":[0.9915192,0.0001571007,0.006659592,0.00019233524,0.000008414376,0.000024288654,0.00020557432,0.0000047682984,0.0012286903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940103,0.000020527319,0.00011968517,0.00026258643,0.00006561802,0.00013053192],"domain_scores_gemma":[0.9997315,0.000020211093,0.000034051147,0.00016379319,0.000012565203,0.00003789315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001433126,0.00006770371,0.00007986038,0.000012780125,0.0001788516,0.000043628974,0.00007770763,0.0000486965,0.0002862407],"category_scores_gemma":[0.00004576605,0.000055535435,0.000022913513,0.00007236728,0.000051574218,0.00015511252,0.00008371795,0.000080242615,0.0000054724756],"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":[0.00006523567,0.000092176604,0.0059123747,0.00004747866,0.000021347143,4.7314586e-7,0.0003156323,0.9120027,0.0054034805,0.0006945297,0.0011335608,0.074311],"study_design_scores_gemma":[0.00027086408,0.000015473555,0.0021177945,0.000008627166,0.000017862265,5.045745e-7,0.000041584273,0.9938586,0.0009315318,0.0010442595,0.0016185609,0.00007437724],"about_ca_topic_score_codex":0.000016396669,"about_ca_topic_score_gemma":0.000029608182,"teacher_disagreement_score":0.94956046,"about_ca_system_score_codex":0.00003503816,"about_ca_system_score_gemma":0.000012507297,"threshold_uncertainty_score":0.3134135},"labels":[],"label_agreement":null},{"id":"W7083582621","doi":"10.1016/j.geoai.2025.100039","title":"Expansive soil characterization: CC/CS ratio method","year":2025,"lang":"en","type":"article","venue":"Geodata and AI.","topic":"Plant Parasitism and Resistance","field":"Agricultural and Biological Sciences","cited_by":1,"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":"Oedometer test; Expansive clay; Swelling; Expansive; Clay soil; Shrinkage; Soil water","score_opus":0.009993524834827508,"score_gpt":0.24728267548096983,"score_spread":0.2372891506461423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083582621","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":[0.98645276,0.0005812461,0.0024133406,0.007527655,0.00024041507,0.00010252457,0.00061265286,0.00004304744,0.0020263381],"genre_scores_gemma":[0.96976614,0.0011881167,0.00043597026,0.0070682457,0.00037302726,0.000019375813,0.0041942815,6.938415e-7,0.016954167],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99952036,0.000040322946,0.000087687724,0.00018465883,0.000053104755,0.00011385091],"domain_scores_gemma":[0.9998042,0.00006161601,0.000024921952,0.000040661176,0.00003542164,0.000033200617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006734733,0.0000640177,0.000096797536,0.000005079281,0.0001690032,0.000079209225,0.000091201626,0.000046759866,0.00007842895],"category_scores_gemma":[0.000017868495,0.000024562254,0.000016176935,0.00015673971,0.000021966658,0.00012718278,0.000050846902,0.000047362537,0.000009800475],"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":[0.000019837027,0.00007862639,0.002015868,0.000015190695,0.000014915259,0.000017657552,0.000032492368,4.587497e-7,0.8657036,0.0040826593,0.0054574627,0.12256123],"study_design_scores_gemma":[0.00039058438,0.000028423725,0.51488954,0.00007317072,0.000019728855,0.000017821241,0.00018449144,0.00023029473,0.024732921,0.00044977086,0.4587816,0.0002016757],"about_ca_topic_score_codex":0.000034871264,"about_ca_topic_score_gemma":0.00015166229,"teacher_disagreement_score":0.8409707,"about_ca_system_score_codex":0.0000024767273,"about_ca_system_score_gemma":0.000009218181,"threshold_uncertainty_score":0.1299853},"labels":[],"label_agreement":null}]}