{"id":"W4392811774","doi":"10.1007/s12665-024-11466-9","title":"Toward a methodology to explore historical groundwater level trends and their origin: the case of Quebec, Canada","year":2024,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Groundwater; Hydrogeology; Representativeness heuristic; Hydrology (agriculture); Environmental science; Hydrograph; Trend analysis; Groundwater resources; Water well; Physical geography; Statistics; Geology; Geography; Cartography; Aquifer; Mathematics; Drainage basin","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001759387,0.0002971372,0.0002219585,0.005368806,0.00264751,0.004118526,0.001403928,0.0006343935,0.00324877],"category_scores_gemma":[0.005091415,0.0001819857,0.0003670247,0.009569549,0.001432049,0.001333381,0.0009143432,0.0005558679,0.0001868663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01983206,"about_ca_system_score_gemma":0.03524356,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9880214,"about_ca_topic_score_gemma":0.9934139,"domain_scores_codex":[0.9995756,0.0001612017,0.00002826668,0.00008254857,0.00006542828,0.00008700212],"domain_scores_gemma":[0.9979875,0.0007891997,0.0001925209,0.0001003915,0.0008127213,0.000117753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009714856,0.0001227692,0.3450869,0.0003440995,0.0002000991,0.002085413,0.03250907,0.04241242,0.003097596,0.2713285,0.01835953,0.2843564],"study_design_scores_gemma":[0.00006510931,0.00008278822,0.3300055,0.0008755163,0.0002861061,0.001033174,0.1225757,0.1959609,0.003588241,0.08632806,0.2590016,0.0001971365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5113017,0.003682651,0.3343009,0.01327154,0.0001015453,0.0007121041,0.01110495,0.0005056355,0.125019],"genre_scores_gemma":[0.7901998,0.001256736,0.1937047,0.0002680721,0.00001495801,0.0001809993,0.001586744,0.00007759371,0.01271032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01983206,"threshold_uncertainty_score":0.1438923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.109517817840151,"score_gpt":0.2713957848297296,"score_spread":0.1618779669895786,"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."}}