{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005402499,0.0003690247,0.0003229964,0.0006916321,0.0001808607,0.0004749337,0.0001520681,0.0004065729,0.0005918928],"category_scores_gemma":[0.0004834058,0.0002046078,0.0004172813,0.0005373035,0.0001756454,0.0003909442,0.0002269341,0.000411512,0.0001549635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000228117,"about_ca_system_score_gemma":0.0001770032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001131597,"about_ca_topic_score_gemma":0.001447374,"domain_scores_codex":[0.9997017,0.00003741216,0.00003782711,0.00006325322,0.0001216035,0.00003833708],"domain_scores_gemma":[0.9995597,0.00007560853,0.0001621794,0.00001830412,0.0001500392,0.00003414775],"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":[0.0001199462,0.00003243443,0.001926916,0.00004402337,0.000007130868,0.00002510913,0.00003940134,0.0001603576,0.9956099,0.00001366418,0.000006055573,0.002015077],"study_design_scores_gemma":[0.000006327478,0.0006964399,0.06095449,0.00002082165,0.00004244239,0.0001071645,0.0001511255,0.002832026,0.9345101,0.00004618788,0.0006061107,0.00002680118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967018,0.000420958,0.002172922,0.00001035466,0.000004201079,0.00003213925,0.000276324,0.00002245591,0.0003588161],"genre_scores_gemma":[0.9913322,0.0007445936,0.006061611,0.00001727514,0.000004904041,0.00006987176,0.0004938141,0.00001715431,0.001258563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001131597,"threshold_uncertainty_score":0.002857208,"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."}}