{"id":"W4399390386","doi":"10.1021/acs.estlett.4c00294","title":"Exploring Outputs of the Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention","year":2024,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pollution prevention; Pollution; Environmental policy; Environmental planning; Science policy; Political science; Environmental science; Public administration; Business; Waste management; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006640111,0.0002345668,0.0001571163,0.0003658815,0.0005377219,0.00006726152,0.0008992006,0.00006919886,0.0001270884],"category_scores_gemma":[0.00008671241,0.0001757455,0.00008158062,0.001863101,0.01367987,0.001253518,0.001606972,0.0003238222,0.00005710396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003134676,"about_ca_system_score_gemma":0.00004036614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005797251,"about_ca_topic_score_gemma":0.000003355441,"domain_scores_codex":[0.9972053,0.00002987513,0.0002878818,0.0007970419,0.001059972,0.0006199889],"domain_scores_gemma":[0.9992124,0.00001584862,0.00009206422,0.0005413707,0.000001177569,0.0001370758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009312743,0.0001011069,0.03492288,0.000007855603,0.000004494443,0.000003479204,0.0004312813,0.0001225349,0.9434511,0.0007411341,0.00003440188,0.02017041],"study_design_scores_gemma":[0.0001894928,0.0001784707,0.231822,0.0000637336,0.00001480653,0.00003792892,0.002100753,0.0003037672,0.7635267,0.0007349274,0.0007912839,0.0002361751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930397,0.00004912197,0.00006631877,0.005230166,0.0002340098,0.0003671143,0.000009909993,0.00007077443,0.0009328587],"genre_scores_gemma":[0.9991401,0.00005389078,0.0001707639,0.0004034451,0.00003005251,0.00003966582,7.975276e-7,0.00001502963,0.0001462059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1968991,"threshold_uncertainty_score":0.9890043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051166396665935,"score_gpt":0.2375780653224701,"score_spread":0.2170664013558108,"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."}}