{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007932322,0.0007323812,0.0005735797,0.001909452,0.008510776,0.006911555,0.002856337,0.001695702,0.002108654],"category_scores_gemma":[0.01129873,0.0003477089,0.0005185721,0.005217455,0.005430005,0.002255177,0.00393061,0.002724124,0.0002017061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1130347,"about_ca_system_score_gemma":0.2919462,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958404,"about_ca_topic_score_gemma":0.9990295,"domain_scores_codex":[0.9956173,0.001384378,0.0002118737,0.0002860703,0.001691041,0.0008093403],"domain_scores_gemma":[0.9865025,0.004328317,0.0003038983,0.0004472956,0.007178922,0.001239023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000259109,0.0003586524,0.1686739,0.001743965,0.0003170041,0.004775187,0.03242154,0.03998121,0.003081631,0.05423691,0.08279837,0.6113525],"study_design_scores_gemma":[0.0001752924,0.0002910159,0.2914238,0.00469782,0.0005450991,0.0008403359,0.1990953,0.03624137,0.005014476,0.09047633,0.3706598,0.000539245],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4963912,0.0297614,0.02457355,0.3035831,0.0005630574,0.001282939,0.004222564,0.0004933524,0.1391288],"genre_scores_gemma":[0.8904994,0.02329759,0.06381889,0.008194706,0.00008699304,0.0002720196,0.001004508,0.00009367448,0.01273234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1130347,"threshold_uncertainty_score":0.8201282,"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."}}