{"id":"W7081961522","doi":"10.11159/icepr25.192","title":"Impact of Power Plants on Ambient PM2.5 in West Bengal, India","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Power (physics); Production (economics); Work (physics); Government (linguistics); Power station","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00009962233,0.0003009487,0.0001701717,0.0004332183,0.0005479734,0.0009582936,0.0004926055,0.0002090883,0.002263458],"category_scores_gemma":[0.0002617961,0.0001085221,0.0002280079,0.0009403788,0.0003005819,0.0004002244,0.000462371,0.0002394587,0.0004013046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009807004,"about_ca_system_score_gemma":0.0004920784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1129021,"about_ca_topic_score_gemma":0.1036575,"domain_scores_codex":[0.9997962,0.00003425353,0.000009950661,0.00004011157,0.00005722683,0.00006239164],"domain_scores_gemma":[0.9998229,0.00004384529,0.00004117556,0.00001471016,0.0000530393,0.00002438492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008342945,0.000853679,0.851855,0.0005749924,0.0003134753,0.006949359,0.005360971,0.01836812,0.05301743,0.001316693,0.004334746,0.0562212],"study_design_scores_gemma":[0.000006254259,0.0001607704,0.9851928,0.00001827819,0.0000595095,0.0002873102,0.004729349,0.003136889,0.003797617,0.0001072816,0.002477524,0.0000265075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956111,0.000166562,0.0001452548,0.0001001229,0.000008402834,0.00001258495,0.0006563857,0.00002436999,0.003275238],"genre_scores_gemma":[0.9989556,0.0001127344,0.00007641819,0.0000167414,0.000004818598,0.000004629128,0.0002221542,0.000005189656,0.0006016873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1129021,"threshold_uncertainty_score":0.2244898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0102841332088316,"score_gpt":0.2530915246170036,"score_spread":0.242807391408172,"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."}}