{"id":"W6993091063","doi":"","title":"Nyckeltalsanalys som underlag för processoptimering och energieffektivisering i kommunala avloppsvattenreningsverk","year":2014,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Wastewater Treatment and Reuse","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wastewater; Sewage treatment; Effluent; Performance indicator; Process (computing); Plant efficiency; Efficient energy use","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004127212,0.0003501772,0.0003808905,0.0004605209,0.0003181631,0.00008439652,0.001242533,0.0002243048,0.0007311036],"category_scores_gemma":[0.0003307524,0.0003073351,0.0001009608,0.001203603,0.0006764321,0.001131132,0.001240496,0.00030944,0.0004480209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001426438,"about_ca_system_score_gemma":0.00003430797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002132431,"about_ca_topic_score_gemma":0.00008552545,"domain_scores_codex":[0.9977412,0.00003830299,0.000560539,0.0007133288,0.0004380699,0.0005086243],"domain_scores_gemma":[0.9979735,0.00002856392,0.0004075642,0.001365323,0.00007434117,0.000150724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002123614,0.003194862,0.3896621,0.0006185495,0.001093332,0.00005525092,0.0005626237,0.0361524,0.3253986,0.06815062,0.08126736,0.09363196],"study_design_scores_gemma":[0.003855924,0.0004922156,0.01396028,0.0004757364,0.0004940057,0.00005343019,0.0002604664,0.0354239,0.422388,0.001580352,0.5188755,0.002140214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485583,0.0001136936,0.01470892,0.006086842,0.0003026483,0.0005380904,0.00008039872,0.0008452999,0.02876579],"genre_scores_gemma":[0.9679901,0.00003529486,0.02842261,0.0001130878,0.00004446151,0.0001218343,0.0007338019,0.00003445591,0.002504363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4376082,"threshold_uncertainty_score":0.9999379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227498909396974,"score_gpt":0.2310067377186306,"score_spread":0.2187317486246609,"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."}}