Ten principles for generating accessible and useable COVID‐19 environmental science and a fit‐for‐purpose evidence base
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it