{"id":"W2053481674","doi":"10.1068/a35156","title":"GIS–Multicriteria Evaluation with Ordered Weighted Averaging (OWA): Case Study of Developing Watershed Management Strategies","year":2003,"lang":"en","type":"article","venue":"Environment and Planning A Economy and Space","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":191,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fanshawe College; Toronto and Region Conservation Authority; Western University","funders":"","keywords":"Parameterized complexity; Watershed; Function (biology); Computer science; Set (abstract data type); Transformation (genetics); Data mining; Mathematical optimization; Mathematics; Machine learning; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.002105899,0.0002034836,0.0003087281,0.0002460197,0.0002698778,0.0003762241,0.0001102889,0.00004282005,0.0002201813],"category_scores_gemma":[0.00004100932,0.000152646,0.00001839091,0.0001120402,0.00006862868,0.0004249647,0.00009187891,0.00007945595,0.000006571885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000362195,"about_ca_system_score_gemma":0.0000204566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003301339,"about_ca_topic_score_gemma":0.00002081343,"domain_scores_codex":[0.9980369,0.0003234952,0.0004819775,0.0005628173,0.00038589,0.0002089605],"domain_scores_gemma":[0.9990199,0.0003312366,0.0002340615,0.0003049297,0.0000346094,0.00007524675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0008083264,0.0007139457,0.6859978,0.0001718357,0.0007895071,0.004080844,0.1671481,0.04033345,0.0007824824,0.00436835,0.0004526443,0.09435274],"study_design_scores_gemma":[0.01217566,0.0007737202,0.05806658,0.000277808,0.0003033592,0.001091143,0.7779351,0.114853,0.0006387123,0.007183445,0.02535378,0.001347732],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980933,0.0002064044,0.01657507,0.00007770389,0.00005974087,0.0005621405,0.000001911087,0.00001136681,0.001572722],"genre_scores_gemma":[0.9845321,0.0000137349,0.01518072,0.00002539015,0.00001144046,0.00002934617,0.000003439686,0.00001025245,0.0001935844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6279312,"threshold_uncertainty_score":0.6224722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004777202162674,"score_gpt":0.347490874293913,"score_spread":0.2470131540776456,"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."}}