{"meta":{"query_hash":"6306b2e085e8","filters":{"venue":"Journal of Electronics Computer Networking and Applied Mathematics"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/6306b2e085e8","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Electronics+Computer+Networking+and+Applied+Mathematics"},"results":[{"id":"W4412693616","doi":"10.55529/jecnam.52.1.15","title":"Bibliometric analysis on machine learning in climate change article during ten years","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics Computer Networking and Applied Mathematics","topic":"Energy, Environment, Economic Growth","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":"Climate change; Data science; Computer science; Artificial intelligence; Oceanography; Geology","score_opus":0.016202357728459155,"score_gpt":0.2097117764730437,"score_spread":0.19350941874458455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412693616","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":[0.82229155,0.034378737,0.012600132,0.002875121,0.000875791,0.0005176894,0.059040986,0.00092285295,0.06649722],"genre_scores_gemma":[0.9559842,0.011477422,0.0045901127,0.00013642311,0.00051366125,0.00034557388,0.021349637,0.00010785927,0.0054952255],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9896678,0.0012111521,0.0017691045,0.0010687996,0.005689425,0.0005937451],"domain_scores_gemma":[0.9797347,0.008218541,0.004090023,0.0008110167,0.006601926,0.0005439226],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004650567,0.00070542947,0.0010623094,0.06933406,0.0013927004,0.0038998707,0.0008059661,0.00062729896,0.0037575176],"category_scores_gemma":[0.031231087,0.00022770329,0.0017180019,0.104455955,0.0005923056,0.0033196816,0.0012654859,0.00047574795,0.0010884897],"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":[0.00030406448,0.0002422867,0.57281685,0.00627685,0.0017281022,0.0012079721,0.002157582,0.013026589,0.0018122466,0.00804045,0.03370278,0.35868418],"study_design_scores_gemma":[0.00005358821,0.00031128543,0.82424337,0.0016884693,0.0019995521,0.0018353367,0.004336025,0.039628822,0.0050165146,0.00883585,0.11183667,0.00021464041],"about_ca_topic_score_codex":0.009855127,"about_ca_topic_score_gemma":0.012016285,"teacher_disagreement_score":0.93066597,"about_ca_system_score_codex":0.002637706,"about_ca_system_score_gemma":0.0032818397,"threshold_uncertainty_score":0.024594843},"labels":[],"label_agreement":null}]}