{"id":"W2997055417","doi":"","title":"Collaborative watershed-based decision-making: Understanding land use-related risk to drinking water sources","year":2011,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Environmental planning; Land use; Environmental resource management; Water resource management; Environmental science; Geography; Computer science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005149898,0.0004210472,0.0003036941,0.001358843,0.003243015,0.006683528,0.001332773,0.001503166,0.002595849],"category_scores_gemma":[0.01055703,0.0002280422,0.000468757,0.001309872,0.007587198,0.004996923,0.0032121,0.00154931,0.0001404458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007926437,"about_ca_system_score_gemma":0.008230806,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03714987,"about_ca_topic_score_gemma":0.05352245,"domain_scores_codex":[0.9971028,0.00185383,0.00008053941,0.0002502655,0.0003742554,0.0003382345],"domain_scores_gemma":[0.9935092,0.004757934,0.0007871421,0.000184294,0.000407851,0.0003534537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001258114,0.0005177623,0.05235079,0.0006247389,0.00009434044,0.001413138,0.5934236,0.01902416,0.002037777,0.2112078,0.002385472,0.1167946],"study_design_scores_gemma":[0.00005904214,0.0002248867,0.03730931,0.0007330999,0.0001054554,0.0003242689,0.640866,0.04485831,0.002031128,0.2170132,0.0563962,0.00007905837],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8780997,0.0007257343,0.03581228,0.008302995,0.00003533334,0.0002440643,0.0000856373,0.00002932918,0.07666486],"genre_scores_gemma":[0.990499,0.0003770869,0.007879191,0.00007921807,0.000006139302,0.00004348283,0.00002533082,0.000005646197,0.001084893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9628502,"threshold_uncertainty_score":0.07386726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698952727590257,"score_gpt":0.2364668906309785,"score_spread":0.2194773633550759,"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."}}