{"id":"W2074260210","doi":"10.1002/ldr.589","title":"A decision support system for soil and water conservation measures on agricultural watersheds","year":2004,"lang":"en","type":"article","venue":"Land Degradation and Development","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Soil conservation; Watershed; Environmental science; Cropping; Land use; Watershed management; Decision support system; Agriculture; Water resource management; Hydrology (agriculture); Environmental resource management; Computer science; Engineering; Ecology; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002472302,0.0009390778,0.0007180623,0.001445727,0.0007186842,0.002516305,0.001609026,0.0011309,0.01640698],"category_scores_gemma":[0.005766717,0.0004437467,0.0007469627,0.0008632306,0.0004681576,0.001588846,0.001329266,0.0008970646,0.002544638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062529,"about_ca_system_score_gemma":0.00121219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004636174,"about_ca_topic_score_gemma":0.003299757,"domain_scores_codex":[0.9991444,0.0002947731,0.0001343715,0.0001511756,0.0002024447,0.00007274817],"domain_scores_gemma":[0.9971342,0.001999252,0.0001545722,0.0001460046,0.0004242039,0.0001417767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002313921,0.0008643394,0.007461112,0.0008467744,0.0001893584,0.002045955,0.001276736,0.5833805,0.01870617,0.04044827,0.03135667,0.3111101],"study_design_scores_gemma":[0.0002606805,0.0001038827,0.0004485367,0.00008176012,0.00003352886,0.0000671033,0.000103593,0.9695313,0.005991704,0.01003763,0.01330981,0.00003042273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06371285,0.0001486514,0.8626686,0.00079529,0.0001021977,0.001048123,0.004872213,0.054504,0.01214805],"genre_scores_gemma":[0.4526021,0.0002436931,0.5329445,0.0002910903,0.00004705699,0.001518125,0.005339344,0.0007963908,0.006217781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01640698,"threshold_uncertainty_score":0.05488682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686559000710177,"score_gpt":0.2082658259545258,"score_spread":0.1914002359474241,"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."}}