{"id":"W2991243918","doi":"10.1289/isesisee.2018.s02.01.32","title":"Using Health Impact Assessments to Assess Potential Health Impacts of Local Infrastructure Projects: A Case Study","year":2018,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intrinsik (Canada)","funders":"","keywords":"Health impact assessment; Environmental planning; Business; Public health; Scope (computer science); Environmental health; Environmental impact assessment; Environmental resource management; Impact assessment; Political science; Geography; Environmental science; Medicine; Computer science; Nursing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008746915,0.0004610609,0.0006732678,0.0001267889,0.0004890537,0.0001419328,0.0004347951,0.0001312564,0.001148876],"category_scores_gemma":[0.00003406856,0.0004019118,0.0001260485,0.0004934487,0.0004163366,0.0007555576,0.00045223,0.0003502934,0.0001046728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176018,"about_ca_system_score_gemma":0.0007643396,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05603311,"about_ca_topic_score_gemma":0.006126347,"domain_scores_codex":[0.9960774,0.0003331892,0.0008996145,0.0006672453,0.0009746464,0.001047923],"domain_scores_gemma":[0.9976237,0.00003694416,0.0007300258,0.0005335165,0.00004866847,0.001027085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006530042,0.008129979,0.6243775,0.0002599197,0.0006906374,0.002543303,0.05093677,0.123545,0.05320203,0.00002301198,0.004166448,0.1314723],"study_design_scores_gemma":[0.00108973,0.004456661,0.9706516,0.0001026617,0.00003203694,0.000490404,0.02118425,0.0005613773,0.0008739076,0.0000814908,0.00003914734,0.0004367437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912071,0.000011588,0.006070204,0.0001391717,0.0002805428,0.001558385,0.00009106909,0.00004519106,0.0005968008],"genre_scores_gemma":[0.9963415,0.000008995454,0.002805302,0.000628593,0.0001134637,0.00001616538,0.00001475551,0.00004212922,0.00002907945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.346274,"threshold_uncertainty_score":0.9998433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08175595832021451,"score_gpt":0.4272569084439033,"score_spread":0.3455009501236888,"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."}}