{"id":"W4391871139","doi":"10.1016/j.jwpe.2024.105692","title":"Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning","year":2024,"lang":"en","type":"preprint","venue":"Journal of Water Process Engineering","topic":"AI in cancer detection","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Vlaamse regering; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Convolutional neural network; Transfer of learning; Settling; Deep learning; Artificial intelligence; Computer science; Activated sludge; Microscopy; Artificial neural network; Materials science; Environmental science; Physics; Optics; Sewage treatment; Environmental engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004489599,0.0005476878,0.0003335038,0.001143836,0.000148525,0.0006500673,0.0003941016,0.0007615308,0.0008469724],"category_scores_gemma":[0.001148931,0.0002185502,0.0004168132,0.0004334428,0.0001878012,0.0004950605,0.0002648828,0.0003437362,0.0003147169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954779,"about_ca_system_score_gemma":0.0004057251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00529163,"about_ca_topic_score_gemma":0.005712401,"domain_scores_codex":[0.9998952,0.00001089958,0.000007639365,0.00003057682,0.00003563355,0.00001998898],"domain_scores_gemma":[0.999437,0.0002102189,0.00006455828,0.00003479896,0.0002184566,0.00003487245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003385907,0.0006363114,0.03245308,0.0006603627,0.000279963,0.0006618081,0.000159644,0.1689358,0.4592388,0.0005326675,0.002429554,0.330626],"study_design_scores_gemma":[0.00001917158,0.0001363887,0.01465499,0.0000108485,0.0000366412,0.00008693099,0.00004640646,0.913439,0.07111266,0.0001680698,0.0002717673,0.00001718417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9514335,0.0005048609,0.04488836,0.0001236287,0.00004666772,0.00004619551,0.0004814673,0.001123744,0.001351767],"genre_scores_gemma":[0.986394,0.000162146,0.01200536,0.00002292471,0.00001116136,0.00001033065,0.0004318737,0.00005861974,0.0009036991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00529163,"threshold_uncertainty_score":0.01052165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144453071109233,"score_gpt":0.2443246662430125,"score_spread":0.2328801355319201,"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."}}