{"id":"W4385358369","doi":"10.1002/lob.10590","title":"Another Step Toward “Big” Catchment Science","year":2023,"lang":"en","type":"article","venue":"Limnology and Oceanography Bulletin","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Chapel; State (computer science); Library science; Art history; History; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004620912,0.0001439153,0.0001462109,0.0002007992,0.0004942819,0.00001981573,0.0002737682,0.00007979463,0.0006648428],"category_scores_gemma":[0.00001891878,0.0001207186,0.00004591438,0.0007004366,0.002171941,0.00004656166,0.0005763927,0.0001228397,0.001721551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001338474,"about_ca_system_score_gemma":0.000003216873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004027986,"about_ca_topic_score_gemma":0.00001171127,"domain_scores_codex":[0.9986799,0.00004283555,0.000139168,0.000457498,0.0001654695,0.0005151642],"domain_scores_gemma":[0.9996433,0.00003912935,0.00004046466,0.000210832,0.000005332993,0.00006090206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004933644,0.0000681407,0.9149199,0.00001802094,0.00005990487,0.00004808524,0.001486131,0.00006178973,0.0005628351,0.001259085,0.07424936,0.007217471],"study_design_scores_gemma":[0.0004469611,0.0001709083,0.6116898,0.000007053624,0.0000277786,0.000006994875,0.0004745491,0.00006340319,0.0004933077,0.001450725,0.3849266,0.0002418742],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9565327,0.00014452,0.0001312434,0.01773731,0.0003592577,0.0001948847,0.000002609304,0.0002331031,0.02466442],"genre_scores_gemma":[0.9951336,0.000492581,0.0002987888,0.002433008,0.00002799941,0.00002274464,0.000002476641,0.000008119546,0.001580713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3106773,"threshold_uncertainty_score":0.9990557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273250839065431,"score_gpt":0.2157556282048696,"score_spread":0.2030231198142153,"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."}}