{"id":"W2967412840","doi":"10.1177/0734242x19864638","title":"Deinking sludge compost stability and maturity assessment using Fourier transform infrared spectroscopy and thermal analysis","year":2019,"lang":"en","type":"article","venue":"Waste Management & Research The Journal for a Sustainable Circular Economy","topic":"Composting and Vermicomposting Techniques","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Université du Québec en Abitibi-Témiscamingue","funders":"Mitacs","keywords":"Deinking; Compost; Fourier transform infrared spectroscopy; Maturity (psychological); Fourier transform; Infrared; Thermal stability; Materials science; Environmental science; Analytical Chemistry (journal); Pulp and paper industry; Chemistry; Mathematics; Waste management; Chemical engineering; Waste paper; Environmental chemistry; Engineering; Physics; Optics; Mathematical analysis","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.006646892,0.0001898088,0.0003216647,0.0001503995,0.001600878,0.0008029111,0.0004410916,0.00006323725,0.00008867924],"category_scores_gemma":[0.00002971268,0.00008746431,0.0001617177,0.0005656722,0.0001465716,0.0003927543,0.0004186384,0.0005042583,6.202241e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003072688,"about_ca_system_score_gemma":0.00002975577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002383311,"about_ca_topic_score_gemma":0.00002131284,"domain_scores_codex":[0.9977598,0.0004462987,0.0003516313,0.0003576671,0.0003125166,0.0007720444],"domain_scores_gemma":[0.9987121,0.0005094437,0.0001390822,0.0001853107,0.0003059208,0.0001481033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003345577,0.001840744,0.4699352,0.008303322,0.01057072,0.0004893934,0.005521504,0.003392972,0.358499,0.04924673,0.002567194,0.08628769],"study_design_scores_gemma":[0.004641779,0.004034776,0.1891933,0.0005127631,0.00258533,0.0003235451,0.1238892,0.3406561,0.01131158,0.1838476,0.1365171,0.002486818],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902554,0.0002789996,0.0008433868,0.0009571423,0.00002558883,0.001577047,0.000007234837,0.00003710174,0.006018065],"genre_scores_gemma":[0.9977103,0.0001020162,0.001567988,0.00005761526,0.00008556656,0.00002775003,0.00001416528,0.000004004132,0.0004306063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3471874,"threshold_uncertainty_score":0.9996989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03920475290554185,"score_gpt":0.3178850536699107,"score_spread":0.2786803007643688,"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."}}