{"id":"W1796485570","doi":"10.1002/ieam.1418","title":"A framework for assessing cumulative effects in watersheds: An introduction to Canadian case studies","year":2013,"lang":"en","type":"article","venue":"Integrated Environmental Assessment and Management","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of British Columbia; University of Waterloo; University of New Brunswick; Dalhousie University; University of Saskatchewan","funders":"","keywords":"Cumulative effects; Watershed; Terminology; Environmental science; Environmental resource management; Computer science; Operations research; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034862,0.0003999222,0.0003375768,0.0001937938,0.0003799743,0.0002053092,0.0001732569,0.0001195588,0.0006545295],"category_scores_gemma":[0.00001490246,0.0003521738,0.0000579606,0.0002365862,0.0001987083,0.001157174,0.0002739099,0.0002269147,0.0001237689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003046195,"about_ca_system_score_gemma":0.00001077918,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01803961,"about_ca_topic_score_gemma":0.01076209,"domain_scores_codex":[0.9977106,0.0001436294,0.0003775799,0.0007719095,0.0003022288,0.0006940162],"domain_scores_gemma":[0.9990597,0.00008152844,0.00008600578,0.0002896087,0.000004474333,0.0004787024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001161946,0.002682081,0.5137342,0.0003743326,0.001006143,0.002025666,0.01563359,0.006301056,0.01454854,0.01267059,0.01230055,0.4186071],"study_design_scores_gemma":[0.002430536,0.001266926,0.8817163,0.0002019384,0.0002508533,0.0001401412,0.06084695,0.00700663,0.000869433,0.03109657,0.01233446,0.001839272],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879049,0.00002846798,0.005504406,0.002429591,0.0003470643,0.002944882,0.0000178087,0.0000412564,0.00078162],"genre_scores_gemma":[0.9444546,0.00008741555,0.05244319,0.00119146,0.00009124559,0.001172972,0.00008061216,0.00004182018,0.0004367237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4167678,"threshold_uncertainty_score":0.999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766388021714223,"score_gpt":0.3269378760606394,"score_spread":0.3092739958434972,"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."}}