{"id":"W2021373096","doi":"10.1142/s1464333211004012","title":"ADVANCING WATERSHED CUMULATIVE EFFECTS ASSESSMENT AND MANAGEMENT: LESSONS FROM THE SOUTH SASKATCHEWAN RIVER WATERSHED, CANADA","year":2011,"lang":"en","type":"article","venue":"Journal of Environmental Assessment Policy and Management","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Australian Government","keywords":"Watershed; Cumulative effects; Context (archaeology); Environmental planning; Watershed management; CLARITY; Environmental resource management; Stressor; Geography; Environmental science; Ecology; Psychology; Computer science","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":[0.000541011,0.0005070475,0.0004798936,0.00008949788,0.0005349066,0.00009288319,0.0004941482,0.00008310992,0.0004582439],"category_scores_gemma":[0.000004103344,0.0003617619,0.0001360071,0.0001431468,0.0005414155,0.0006716589,0.001259042,0.0003736239,0.00001248189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001815189,"about_ca_system_score_gemma":0.00004318205,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04736339,"about_ca_topic_score_gemma":0.01110956,"domain_scores_codex":[0.9967574,0.0002761234,0.0006851885,0.0005231459,0.00104855,0.0007095538],"domain_scores_gemma":[0.9985377,0.0001141365,0.0005512555,0.0003728963,0.000003450921,0.0004205836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003431836,0.002045975,0.8483168,0.0003064052,0.003794442,0.00145188,0.03400239,0.0004519537,0.005474095,0.003589507,0.005750162,0.09447318],"study_design_scores_gemma":[0.001933289,0.0002652551,0.9750372,0.0000842322,0.0004393933,0.00001889809,0.01256708,0.00008379325,0.0003280814,0.002150761,0.006653077,0.0004389703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848027,0.000102642,0.001119273,0.001546523,0.0002657443,0.0009473387,0.00008420638,0.00001596715,0.01111567],"genre_scores_gemma":[0.9845647,0.001729252,0.0105985,0.001953438,0.0001274883,0.00003784365,0.0000229972,0.00004352367,0.0009223029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1267204,"threshold_uncertainty_score":0.9998834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164063430982454,"score_gpt":0.2660295011255602,"score_spread":0.2543888668157356,"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."}}