{"id":"W2991516990","doi":"","title":"Adapting regional watershed management to climate change in Bavaria and Québec","year":2013,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Climate change; Watershed; Geography; Environmental science; Physical geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001361931,0.0002015792,0.0002828675,0.0008652111,0.001868626,0.002715493,0.001313484,0.0007449857,0.002317855],"category_scores_gemma":[0.003241532,0.0001995053,0.0003026284,0.00201362,0.0007786671,0.0005162125,0.0009974013,0.0005704592,0.0001734145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06795032,"about_ca_system_score_gemma":0.05405569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947782,"about_ca_topic_score_gemma":0.9980161,"domain_scores_codex":[0.9987358,0.0003590817,0.00003956581,0.0001284965,0.0002090813,0.0005279715],"domain_scores_gemma":[0.9987104,0.0001604502,0.00009199075,0.00005346526,0.0005554121,0.0004282245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008843052,0.0007431137,0.5410361,0.0002645791,0.0007660544,0.001583939,0.003255795,0.1321079,0.009654139,0.01317774,0.05670688,0.2398194],"study_design_scores_gemma":[0.0001295653,0.00008322213,0.9007209,0.0001154832,0.0001402344,0.00007668083,0.006218427,0.03492928,0.0009611476,0.0008295284,0.05570701,0.00008857962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602547,0.001621376,0.002012414,0.006435888,0.0001099756,0.0002122648,0.002926993,0.0001652655,0.02626114],"genre_scores_gemma":[0.9897798,0.0006769614,0.001775099,0.0003828098,0.00001079971,0.00005572031,0.0006911404,0.00002922495,0.006598257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06795032,"threshold_uncertainty_score":0.4930164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767382644484086,"score_gpt":0.242109780813527,"score_spread":0.2144359543686861,"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."}}