{"id":"W4390650436","doi":"10.15666/aeer/2106_60416057","title":"TERRESTRIAL BIOSPHERE WATER BALANCE ANALYSIS: A MATHEMATICAL MODEL TO PREDICT THE IMPACTS OF CLIMATE CHANGE ON NET WATER BUDGET ON GLOBAL SCALE","year":2023,"lang":"en","type":"article","venue":"Applied Ecology and Environmental Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biosphere; Water balance; Environmental science; Scale (ratio); Climate change; Balance (ability); Climatology; Net (polyhedron); Hydrology (agriculture); Environmental resource management; Ecology; Geography; Geology; Mathematics; Biology","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":[0.001175336,0.0001803866,0.0002766599,0.00007884903,0.0004494018,0.0000156909,0.0003009494,0.0001361272,0.0007458532],"category_scores_gemma":[0.000008972628,0.00009583118,0.00006096276,0.0002391843,0.0008189317,0.00005266863,0.001226824,0.0002301062,0.003424044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113199,"about_ca_system_score_gemma":0.000001385911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003508108,"about_ca_topic_score_gemma":0.0001231729,"domain_scores_codex":[0.9978345,0.0001241461,0.0002217513,0.0004851889,0.0004181948,0.0009161981],"domain_scores_gemma":[0.9993989,0.0001123212,0.00002227039,0.0003346491,9.327525e-7,0.0001308855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00466416,0.001657996,0.8360826,0.00009516309,0.001149596,0.0000830182,0.009367367,0.1105904,0.01982512,0.001544429,0.009034536,0.00590564],"study_design_scores_gemma":[0.002077741,0.001685755,0.9115463,0.00001866744,0.0002765491,0.000003586032,0.001322993,0.05572974,0.009706144,0.01605561,0.001097274,0.0004795816],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885414,0.000003646986,0.00001219333,0.002972805,0.00003025197,0.000721239,0.00005110576,0.00002509427,0.00764226],"genre_scores_gemma":[0.9983657,0.0002207442,0.00002798026,0.0007195722,0.00003325868,0.0003525176,0.0000564611,0.00001066837,0.0002131146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07546381,"threshold_uncertainty_score":0.9973519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02695435833133639,"score_gpt":0.289320034840011,"score_spread":0.2623656765086746,"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."}}