Conservation physiology and the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic and associated public health measures have had unanticipated effects on ecosystems and biodiversity. Conservation physiology and its mechanistic underpinnings are well positioned to generate robust data to inform the extent to which the Anthropause has benefited biodiversity through alterations in disturbance-, pollution- and climate change-related emissions. The conservation physiology toolbox includes sensitive biomarkers and tools that can be used both retroactively (e.g. to reconstruct stress in wildlife before, during and after lockdown measures) and proactively (e.g. future viral waves) to understand the physiological consequences of the pandemic. The pandemic has also created new risks to ecosystems and biodiversity through extensive use of various antimicrobial products (e.g. hand cleansers, sprays) and plastic medical waste. Conservation physiology can be used to identify regulatory thresholds for those products. Moreover, given that COVID-19 is zoonotic, there is also opportunity for conservation physiologists to work closely with experts in conservation medicine and human health on strategies that will reduce the likelihood of future pandemics (e.g. what conditions enable disease development and pathogen transfer) while embracing the One Health concept. The conservation physiology community has also been impacted directly by COVID-19 with interruptions in research, training and networking (e.g. conferences). Because this is a nascent discipline, it will be particularly important to support early career researchers and ensure that there are recruitment pathways for the next generation of conservation physiologists while creating a diverse and inclusive community. We remain hopeful for the future and in particular the ability of the conservation physiology community to deliver relevant, solutions-oriented science to guide decision makers particularly during the important post-COVID transition and economic recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".