{"id":"W2074413632","doi":"10.1016/j.watres.2005.04.072","title":"Sludge based Bacillus thuringiensis biopesticides: Viscosity impacts","year":2005,"lang":"en","type":"article","venue":"Water Research","topic":"Insect Resistance and Genetics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Intégré de Santé et de Services Sociaux des Laurentides; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biopesticide; Fermentation; Bacillus thuringiensis; Raw material; Industrial fermentation; Chemistry; Food science; Chemical oxygen demand; Biochemical oxygen demand; Pulp and paper industry; Biotechnology; Environmental science; Environmental engineering; Agronomy; Sewage treatment; Biology; Pesticide; Bacteria; Organic chemistry","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":[0.0007358147,0.0001433507,0.0001162352,0.0001171274,0.000226102,0.00009215088,0.0003045843,0.0001479216,0.0001567029],"category_scores_gemma":[0.00008628484,0.0001045821,0.00007950467,0.0001214705,0.0001629709,0.000004618894,0.0002020069,0.000196955,0.0003932861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004612704,"about_ca_system_score_gemma":0.0001081096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000648877,"about_ca_topic_score_gemma":0.000215299,"domain_scores_codex":[0.998154,0.0001413067,0.0001656153,0.0003790866,0.0003854599,0.0007745314],"domain_scores_gemma":[0.9990192,0.000008565894,0.00001521555,0.0005424002,0.0002458547,0.0001687619],"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":[0.0001313276,0.00007505013,0.001171317,0.00002706426,0.00001932155,0.00001214034,0.00008504906,0.0002214822,0.9927257,0.00001261374,0.003796777,0.001722173],"study_design_scores_gemma":[0.0003500334,0.0002480323,0.0019125,0.00001199861,0.000004384134,0.000005482624,0.00002015833,0.0001348275,0.8634593,0.00002855806,0.1336778,0.0001468521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947999,0.000432356,0.0003110776,0.0009074563,0.00004849496,0.0001953982,0.00001482023,0.00001574277,0.003274736],"genre_scores_gemma":[0.9959323,0.00007728899,0.0009054262,0.0003592174,0.0003929882,0.00001959597,0.00008638819,0.00002663354,0.002200206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1298811,"threshold_uncertainty_score":0.5055028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0380016676077644,"score_gpt":0.3349284235994936,"score_spread":0.2969267559917292,"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."}}