{"id":"W4225574059","doi":"10.1029/2021gh000525","title":"Using Community Science to Better Understand Lead Exposure Risks","year":2022,"lang":"en","type":"article","venue":"GeoHealth","topic":"Noise Effects and Management","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences","funders":"National Science Foundation","keywords":"Environmental health; Logistic regression; Predictive power; Intervention (counseling); Medicine; Statistics; Mathematics; Physics","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01173088,0.001124301,0.0009646997,0.00411649,0.001338965,0.003896303,0.001873788,0.001716911,0.009444512],"category_scores_gemma":[0.05712689,0.0005341758,0.00184592,0.001864994,0.002213179,0.004858872,0.003751753,0.002945349,0.0009892898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002963433,"about_ca_system_score_gemma":0.00470839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04235248,"about_ca_topic_score_gemma":0.04113645,"domain_scores_codex":[0.992908,0.004579579,0.0002138499,0.001092895,0.0009379971,0.0002676595],"domain_scores_gemma":[0.9744431,0.01966612,0.001617211,0.00164497,0.002072223,0.0005562899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002480468,0.001369103,0.2755087,0.002177326,0.001390251,0.001040023,0.01322788,0.09345576,0.001462701,0.2235959,0.02344153,0.3630828],"study_design_scores_gemma":[0.0001728808,0.0006526305,0.06108658,0.001657447,0.0004031436,0.0004150483,0.009967846,0.2226311,0.001016586,0.608314,0.09346556,0.0002171936],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2644569,0.00588159,0.6004964,0.03815415,0.0009464351,0.001878189,0.005371819,0.001315024,0.08149952],"genre_scores_gemma":[0.8362687,0.003805718,0.1464135,0.004342811,0.0004073252,0.001314597,0.00226572,0.0002226176,0.004958972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9981262,"threshold_uncertainty_score":0.08421195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4001885442421698,"score_gpt":0.5229375060778658,"score_spread":0.122748961835696,"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."}}