Bibliographic record
Abstract
Year 2020 has brought the greatest global pandemic to hit the world since the end of the First World War. The severe acute respiratory syndrome coronavirus 2 and the resulting disease named coronavirus disease 2019 has brought the world to its knees both financially and medically. The American Society of Primatologists has postponed their annual meetings from the end of May 2020 until the end of September 2020, while the International Primatological Society have postponed their biennial congress from August 2020 to August 2021, which has also resulted in their 2022 meetings in Malaysia being pushed back until 2023. Here, I explore the potential dangers of pursuing any primate fieldwork during this pandemic on our study species, their ecosystems, and local peoples. I believe that the risk of bringing this virus into our study ecosystems is too great and that primatologists should cancel all field research until the pandemic ends or a vaccine/reliable treatment is widely available. This is the year we all must become One Health practitioners!
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".