From climate change to AMR: understanding environmental-human health issues in a One Health framework
Bibliographic record
Abstract
As the impacts of climate change intensify, our interconnectedness to the environment around us seems ever more apparent. Changing terrestrial landscapes impact adjacent aquatic ecosystems, as the terrestrial-aquatic continuum experiences the ever-pressing stresses of anthropogenic activity. In the Canadian Arctic, ancient carbon stores and contaminants such as methylmercury are emerging as permafrost thaw accelerates, changing their biogeochemical nature, impacting local communities and threatening ecological health in ways still yet to be fully understood. Awakening microorganisms in these once frozen grounds are all too eager to get to work, as scientists continue to try to understand how, where, and why climate change is impacting aquatic ecosystems across Canada. Increasing aquatic nutrient loads and chemical/biological contaminants adjacent to urban and agricultural lands also impact both ecosystem and ultimately human health. In the shadow of a global pandemic, the need to understand how environmental-human interactions impact human health is ever pressing, requiring the collective expertise of researchers across the environmental-human health landscape. Antimicrobial resistance (AMR), despite being a natural evolutionary mechanism for microbial survival in the environment, has been increasing in presence and prevalence in healthcare systems worldwide, resulting in drug-resistant infections that can be fatal. As such, there is a need to understand AMR in both its natural state within the environmental microbial biosphere, alongside those places (i.e., agricultural lands, wastewater treatment outflows etc.) where humans have introduced co-selective agents such as metals, antibiotic residues and other compounds that can further facilitate and even promote resistance activity in the natural environment (Fig. 1). This connection between the human health landscape and the environment around us is a vital part of understanding the risks of both climate change and AMR, requiring an integrated and collaborative One Health approach across disciplines. Here we present research associated with our Genomics Research and Development Initiative programs using novel genomics tools and large-scale laboratory simulations to better understand the impacts of climate change and AMR in a multi-disciplinary environmental context. This work helps fullfil the need to understand the dynamics of these two global threats in an trans-disciplinary nature, drawing on the expertise of environmental microbiologists, hydrologists, bioinformaticians, and water quality experts, in tandem with public health and infectious disease experts to better understand how these threats will evolve as our planet tries to adapt to the complex stressors of the Anthropocene.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".