{"id":"W4409204210","doi":"10.1016/j.infgeo.2025.100010","title":"WISE: A spatially explicit carbon cycling model at the watershed scale","year":2025,"lang":"en","type":"article","venue":"Information Geography","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Cycling; Watershed; Scale (ratio); Carbon cycle; Environmental science; Carbon fibers; Computer science; Geography; Ecology; Cartography; Forestry; Ecosystem; Algorithm; Biology; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002786331,0.0005939042,0.000454866,0.000415934,0.0003485742,0.0006024739,0.001310358,0.0007100307,0.001848047],"category_scores_gemma":[0.0006658787,0.0004094666,0.0007104828,0.0007558556,0.0003546033,0.0007628262,0.0007520097,0.0006504937,0.0002045886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007962392,"about_ca_system_score_gemma":0.001578621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02348357,"about_ca_topic_score_gemma":0.02718243,"domain_scores_codex":[0.9999033,0.00002310007,0.000007999907,0.00002525616,0.00002437911,0.00001593526],"domain_scores_gemma":[0.9998232,0.00007250092,0.00002436349,0.00002031926,0.00003302101,0.00002676917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001632798,0.00002219625,0.001255164,0.00001669257,0.00002720841,0.00003916371,0.00001770315,0.9897292,0.001560798,0.003592278,0.0005679677,0.003155387],"study_design_scores_gemma":[0.00001383944,0.000009972585,0.0003126993,0.000001805295,0.000009299816,0.000009166478,0.000006150697,0.9962554,0.0005484383,0.001134002,0.001692964,0.000006200226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3618185,0.000304892,0.6014196,0.0006083783,0.0001631775,0.0003048289,0.01063375,0.005519228,0.01922756],"genre_scores_gemma":[0.7986469,0.000485373,0.1896057,0.0001187252,0.00003305237,0.0005532552,0.005235221,0.0003129916,0.005008916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02348357,"threshold_uncertainty_score":0.04669374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004497195445518282,"score_gpt":0.1981630903156719,"score_spread":0.1936658948701536,"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."}}