{"id":"W4295064627","doi":"10.3390/ijerph191811171","title":"Cumulative Impacts of Diverse Land Uses in British Columbia, Canada: Application of the “EnviroScreen” Method","year":2022,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research; University of Northern British Columbia","keywords":"Socioeconomic status; Percentile; Ethnic group; Geography; Sustainability; Cumulative effects; Percentile rank; Distribution (mathematics); Health equity; Health impact assessment; Land use; Environmental justice; Environmental resource management; Environmental planning; Environmental health; Environmental science; Public health; Political science; Health care; Medicine; Statistics; Economic growth; Ecology; Population; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001248844,0.0006594974,0.0004448182,0.004053268,0.001772596,0.001994755,0.001347993,0.000289736,0.004809376],"category_scores_gemma":[0.004617617,0.0002298957,0.0006617889,0.006984164,0.0006880731,0.0003923641,0.001938779,0.0005975637,0.000201378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02272604,"about_ca_system_score_gemma":0.02757439,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938774,"about_ca_topic_score_gemma":0.9959077,"domain_scores_codex":[0.9988621,0.0001920867,0.00006594272,0.0002050439,0.0004781072,0.0001967456],"domain_scores_gemma":[0.9981522,0.0004021571,0.0001533516,0.0001377766,0.000981024,0.0001733642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002241029,0.00008703284,0.7731079,0.0003876759,0.0005635932,0.0006488814,0.002310534,0.03300999,0.0004977909,0.008549049,0.02333875,0.1572747],"study_design_scores_gemma":[0.00005195728,0.00005857915,0.8902407,0.000301587,0.0002595071,0.0001807413,0.008519747,0.05440575,0.0008379517,0.004130936,0.04090218,0.0001104062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8586963,0.001381757,0.02516402,0.001337416,0.00007531288,0.0008349094,0.06151744,0.0005147168,0.05047807],"genre_scores_gemma":[0.9529781,0.0006677967,0.02309133,0.0001554136,0.00001163627,0.0003318494,0.01363056,0.00007474034,0.009058606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02272604,"threshold_uncertainty_score":0.1648898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04740072336220157,"score_gpt":0.383736178224191,"score_spread":0.3363354548619895,"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."}}