{"id":"W2738149826","doi":"10.1016/j.wasman.2017.07.018","title":"Benefits to decomposition rates when using digestate as compost co-feedstock: Part I – Focus on physicochemical parameters","year":2017,"lang":"en","type":"article","venue":"Waste Management","topic":"Composting and Vermicomposting Techniques","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"City of Edmonton; Alberta Innovates - Technology Futures","keywords":"Digestate; Compost; Raw material; Chemistry; Municipal solid waste; Aeration; Pulp and paper industry; Waste management; Food waste; Anaerobic digestion; Methane; Organic chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001755284,0.0002558989,0.0002415764,0.00003185914,0.0008391875,0.0004272493,0.0006404865,0.00005434775,0.00002716083],"category_scores_gemma":[0.00002245695,0.0001313491,0.0001104699,0.00007277764,0.00006155955,0.0001172705,0.0004029764,0.000120162,0.0001001324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007436494,"about_ca_system_score_gemma":0.00000249958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006793507,"about_ca_topic_score_gemma":0.00004193281,"domain_scores_codex":[0.9984962,0.00005352431,0.0002503482,0.00050394,0.0002912359,0.0004047797],"domain_scores_gemma":[0.9992168,0.0001146837,0.0001927532,0.0002761064,0.00004443312,0.0001552094],"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":[0.0005909537,0.0007062604,0.003703572,0.0000569778,0.0001574337,0.00006475852,0.0002316453,0.001296561,0.4999506,0.003641797,0.01200405,0.4775954],"study_design_scores_gemma":[0.00139359,0.003497723,0.08502286,0.002825698,0.0002393714,0.00004374186,0.0006329052,0.004167972,0.8512264,0.01141896,0.03698439,0.002546347],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988721,0.00001120637,0.00002230584,0.002931012,0.0001394979,0.000541129,0.00003806523,0.0002190799,0.007376663],"genre_scores_gemma":[0.9965736,0.00001360182,0.002479187,0.0004489722,0.0001845203,0.00003365303,0.00007234308,0.000004155415,0.0001899769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.475049,"threshold_uncertainty_score":0.6454437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05117784130958979,"score_gpt":0.3077931557375844,"score_spread":0.2566153144279946,"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."}}