{"id":"W4398771085","doi":"10.1080/24751448.2024.2313441","title":"Neural Networks for Monitoring Microalgae Biomass in Building Façades","year":2024,"lang":"en","type":"article","venue":"Technology|Architecture + Design","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biomass (ecology); Environmental science; Artificial neural network; Computer science; Artificial intelligence; Ecology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005645326,0.0002595061,0.0002632556,0.0004644454,0.0001510766,0.0001036468,0.0004930446,0.0002984859,0.00001885244],"category_scores_gemma":[0.00006301545,0.0002222462,0.0001252841,0.001340025,0.000224436,0.00009128322,0.0001809258,0.0004988478,0.00002134426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835006,"about_ca_system_score_gemma":0.000007150497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001593553,"about_ca_topic_score_gemma":0.000006230097,"domain_scores_codex":[0.9982185,0.00008166675,0.0002993809,0.0006191749,0.0001657644,0.0006154573],"domain_scores_gemma":[0.9993411,0.0002146758,0.00004263995,0.0003341132,0.000005678575,0.00006174861],"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":[0.00003502572,0.00004465478,0.02946508,0.00006093054,0.00006682342,0.00008047545,0.000396488,0.09727772,0.7297391,0.0001406652,0.0002206687,0.1424724],"study_design_scores_gemma":[0.0005432231,0.0002861022,0.007114518,0.0003233315,0.0001231906,0.00008324763,0.000203403,0.1292968,0.8320999,0.02642057,0.002544243,0.0009614728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6416735,0.001174549,0.3544101,0.001370281,0.0003472255,0.0003174607,0.000003386323,0.0006875342,0.00001596368],"genre_scores_gemma":[0.9629095,0.00001110682,0.03656714,0.00001036852,0.0002145166,0.0001181187,0.000002351149,0.00004004857,0.0001268079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.321236,"threshold_uncertainty_score":0.9062938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167426129187412,"score_gpt":0.2738657654902269,"score_spread":0.2521915041983528,"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."}}