{"id":"W4408111113","doi":"10.5194/hess-29-1183-2025","title":"Learning from a large-scale calibration effort of multiple lake temperature models","year":2025,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; Poul Due Jensens Fond (Grundfos Foundation); Horizon 2020; Bundesministerium für Bildung und Forschung; Global Lake Ecological Observatory Network","keywords":"Calibration; Scale (ratio); Environmental science; Remote sensing; Computer science; Hydrology (agriculture); Statistics; Geology; Geography; Cartography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004477601,0.0009967497,0.0007436688,0.0007791374,0.0005245261,0.001180882,0.001208138,0.001018715,0.000899881],"category_scores_gemma":[0.01262404,0.0006474843,0.00132941,0.001057182,0.0004392804,0.001785773,0.00119657,0.001639018,0.0003714646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075699,"about_ca_system_score_gemma":0.0009839113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008524616,"about_ca_topic_score_gemma":0.01064399,"domain_scores_codex":[0.9988457,0.0005117435,0.00007392187,0.0003771514,0.0001202938,0.00007123955],"domain_scores_gemma":[0.995274,0.002340511,0.0003749864,0.001179993,0.0007141891,0.00011625],"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":[0.00007181098,0.0001148197,0.02250498,0.00005606219,0.0004984342,0.00005664735,0.0000687174,0.9359401,0.001433347,0.0005127718,0.001497744,0.03724455],"study_design_scores_gemma":[0.00001787728,0.00002859752,0.005704685,0.00002134246,0.00006336371,0.00001781016,0.00003133619,0.99107,0.001235772,0.001058983,0.000730921,0.00001932217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.845948,0.0007060543,0.145614,0.0005049155,0.000167923,0.00009774321,0.002103044,0.002519228,0.00233895],"genre_scores_gemma":[0.9672728,0.0001140661,0.02885288,0.00008965025,0.00002914734,0.00006351712,0.003160502,0.0001351721,0.0002823098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008524616,"threshold_uncertainty_score":0.02368015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00796001830675792,"score_gpt":0.2009571298886851,"score_spread":0.1929971115819272,"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."}}