{"meta":{"query_hash":"1ee37dfa08a7","filters":{"venue":"GeoHorizons"},"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/1ee37dfa08a7","api":"https://metacan.xera.ac/api/v1/cohort?venue=GeoHorizons"},"results":[{"id":"W4417227211","doi":"10.1144/gh2025-4","title":"Unravelling the power of neural networks for flood prediction across complex hydrological systems","year":2025,"lang":"en","type":"article","venue":"GeoHorizons","topic":"Hydrological Forecasting Using AI","field":"Environmental 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":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"Flood myth; Hydrometeorology; Artificial neural network; Flooding (psychology); Feature (linguistics); Interpolation (computer graphics); Warning system; Reliability (semiconductor); Set (abstract data type)","score_opus":0.02632417619365111,"score_gpt":0.2681598292556399,"score_spread":0.24183565306198881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417227211","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":[0.9442757,0.000054976634,0.05184757,0.00060580723,0.0007266025,0.0004950423,0.000042145206,0.00008879743,0.0018633594],"genre_scores_gemma":[0.99915504,0.0000027543392,0.0002692544,0.00019672008,0.00007171692,0.000050090333,0.000015960683,0.000007987129,0.00023048361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986871,0.00009476381,0.00031710736,0.00030210265,0.00017086121,0.00042806388],"domain_scores_gemma":[0.99925697,0.00029842812,0.00009759645,0.00028443622,0.000015252176,0.000047321555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063501426,0.00013157155,0.00020078612,0.000013295148,0.0004147869,0.000039073846,0.00033355955,0.00013912284,0.000086845765],"category_scores_gemma":[0.00013511507,0.00008465338,0.0001013405,0.00028700117,0.00038113192,0.00004647974,0.00022035665,0.00019481733,0.000012337222],"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":[0.00004421267,0.000067232504,0.022506567,0.000007773756,0.000021986885,0.0000010907852,0.00012014445,0.9723444,0.00080620026,0.0004566353,0.0021633706,0.0014604081],"study_design_scores_gemma":[0.00026581698,0.00029749135,0.025065366,0.000014385615,0.000027847505,0.000006846469,0.00005316217,0.9660089,0.000050343464,0.000503448,0.007621108,0.00008528847],"about_ca_topic_score_codex":0.00007967685,"about_ca_topic_score_gemma":0.000020340924,"teacher_disagreement_score":0.05487933,"about_ca_system_score_codex":0.000053388067,"about_ca_system_score_gemma":0.0000045447155,"threshold_uncertainty_score":0.34520647},"labels":[],"label_agreement":null}]}