A NOAA Fisheries science perspective on the conditions during and after COVID-19: challenges, observations, and some possible solutions, or why the future is upon us
Bibliographic record
Abstract
Many fisheries and marine science organizations are working to determine how to meet their missions in the midst of the COVID-19 outbreak. As such, it seems prudent to exchange ideas, share knowledge, and initiate a discussion among us. As the scientific leadership team for NOAA Fisheries, we wanted to offer some perspectives. Others are also evaluating the impacts of COVID-19 but from the perspective of addressing tactical, day-to-day concerns of restarting operations for various marine and fisheries-oriented organizations. Thus, it seemed appropriate to us to explore the potential challenges posed by COVID-19 and to purposefully ascertain whether there are strategic opportunities for improving how we conduct our operations. We need to find ways to mitigate the effects of COVID-19 on our mission and also to glean information from our responses while in the midst of the crisis. We offer some recommendations to that end and offer these thoughts not as having solved every problem, but to learn from each other, compare across organizations, and engage in dialogue within our discipline to advance much-needed changes.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".