Editorial: Artificial Intelligence in Environmental Microbiology
Bibliographic record
Abstract
Perhaps twenty-first century is so called “Digital Era” since digitalization and artificial intelligence (AI) is finding its way into every aspect of human life. Nowadays, AI-based approaches are gaining a lot of traction as components of research and development in different scientific and technological fields. One of the areas that is experiencing a digital revolution is environmental microbiology, which is the science of studying the interactions between the microorganisms and the environment and their mutual impacts (Pepper et al., 2011). Approaches such as machine learning (ML), deep learning (DL), image processing, pattern recognition and internet of things (IoT) are being widely implemented in this field in all aspects from theoretical development and identification to process monitoring and optimization (Asgharnejad and Sarrafzadeh, 2020; Gargalo et al., 2020; Asgharnejad et al., 2021). Outbreak of the global challenge of COVID-19 pandemic during the last 2 years and the huge impacts of this virus on socioeconomic infrastructures has also highlighted the necessity of innovative approaches for controlling and monitoring microbial communities in the environment. This special issue provides a platform for gathering the most recent advances in the fields of environmental microbiology from the perspective of AI. It includes 10 scientific papers (six original research articles, two mini-reviews and two reviews) that cover a wide range of AI approaches including ML, DL, and image processing. Two of these papers are specifically focused on using AI for diagnosis and tackling the SARS-CoV-2 virus, which is the species causing COVID-19 and, in this regard, the current Research Topic can be a reference for ongoing research on the edge of science to overcome the pandemic and prevent future such catastrophic outbursts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.032 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".