Ten principles for generating accessible and useable COVID‐19 environmental science and a fit‐for‐purpose evidence base
Bibliographic record
Abstract
1. The 'anthropause', a period of unusually reduced human activity and mobility due to COVID-19 restrictions, has serendipitously opened up unique opportunities for research on how human activities impact the environment. 2. In the field of health, COVID-19 research has led to concerns about the quality of research papers and the underlying research and publication processes due to accelerated peer review and publication schedules, increases in pre-prints and retractions. 3. In the field of environmental science, framing the pandemic and associated global lockdowns as an unplanned global human confinement experiment with urgency should raise the same concerns about the rigorousness and integrity of the scientific process. Furthermore, the recognition of an 'infodemic', an unprecedented explosion of research, risks research waste and duplication of effort, although how information is used is as important as the quality of evidence. This highlights the need for an evidence base that is easy to find and use - that is discoverable, curated, synthesizable, synthesized. 4. We put forward a list of 10 key principles to support the establishment of a reproducible, replicable, robust, rigorous, timely and synthesizable COVID-19 environmental evidence base that avoids research waste and is resilient to the pressures to publish urgently. These principles focus on engaging relevant actors (e.g. local communities, rightsholders) in research design and production, statistical power, collaborations, evidence synthesis, research registries and protocols, open science and transparency, data hygiene (cleanliness) and integrity, peer review transparency, standardized keywords and controlled vocabularies.
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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 | 0.784 | 0.767 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.056 | 0.039 |
| Open science | 0.020 | 0.036 |
| Research integrity | 0.047 | 0.062 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".