{"id":"W4232469640","doi":"10.31223/x5zs5x","title":"Hydrology Research Articles are Becoming More Topically Diverse","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universidad de Cantabria; University of Arizona; Nuclear Safety and Security Commission; University of Washington; University of Alabama; National Aeronautics and Space Administration","keywords":"Popularity; Hydrology (agriculture); Diversity (politics); Water resources; Catchment hydrology; Multidisciplinary approach; Hydrological modelling; Environmental science; Computer science; Sociology; Ecology; Social science; Psychology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001936754,0.00008835666,0.0002453732,0.0001570842,0.0005146299,0.0002588675,0.0003860921,0.0002210396,0.003826354],"category_scores_gemma":[0.001065526,0.0000826006,0.0001735701,0.0003498498,0.0003404913,0.00006144208,0.001085159,0.0005656618,0.00005492121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015019,"about_ca_system_score_gemma":0.0003394725,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01724156,"about_ca_topic_score_gemma":0.01978124,"domain_scores_codex":[0.9970486,0.001371786,0.0002075403,0.0003914017,0.0006603758,0.0003203024],"domain_scores_gemma":[0.9980041,0.0009938144,0.00007106119,0.0002157261,0.0005843486,0.0001309908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003120446,0.0006858083,0.3222705,0.000164056,0.0007883402,0.0004091231,0.1079688,0.007060841,0.0002695164,0.117319,0.01076032,0.4322725],"study_design_scores_gemma":[0.0003001103,0.00002843474,0.3048637,0.0001312377,0.0001187457,0.000001331893,0.1572578,0.01403311,0.0001214751,0.5003719,0.02211396,0.0006582077],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9338459,0.000165826,0.005061419,0.03173588,0.0003279615,0.000178937,0.000001574576,0.00006431642,0.0286182],"genre_scores_gemma":[0.9756065,0.00005825048,0.01678494,0.0004087914,0.00044881,0.00002504668,0.00001077788,0.000005495002,0.006651392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4316142,"threshold_uncertainty_score":0.9981052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3172730956926512,"score_gpt":0.5167985818824574,"score_spread":0.1995254861898063,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}