Microbes Saving Lives and Reducing Suffering
Bibliographic record
Abstract
Given the overexploitation of the resources of planet Earth, due in large part to the ever-increasing human population (https://www.un.org/sustainabledevelopment/sustainable-consumption-production/), which has already compromised vital planetary processes (https://reports.weforum.org/docs/ WEF_ Business_on_the_ Edge_ 2024.pdf), the limitations of which are encapsulated in planetary boundaries (Richardson et al. 2023; Guptaet al. 2024; https://www.pik-potsdam.de/en/news/latest-news/earth -exceed-safe-limit s-first-planetary-healt h-check-issue s-red-alert) and climate tipping points (Wunderling et al. 2023; Wunderling, von der Heydt, and Aksenov 2024), it would not be unexpected that a visitor from Mars might well be confused,or at least bemused, by our efforts to save lives and reduce mor-bidity. The Martian might be similarly bemused when it learned that although warfare is a constant feature of biosphere ecology, including human behaviour, with military personnel of opposing armies doing their best to kill one another, military physicians will try their best to save the lives of injured prisoners of the opposing side. But warfare and other activities of individuals and groups aimed at harming others notwithstanding, saving lives and preventing/reducing human suffering is an ingrained moral-ethical-humanitarian imperative (https://www. ohchr. org/sites/default/files/Documents/ Publications/Factsheet31.pdf). While we cannot prevent death, we try hard to prevent avoidable, premature death and disease. But trying hard is not the same as succeeding (Kruk et al. 2018). This is reflected in the United Nations Sustainable Development Goal(SDG) 3 Ensure healthy lives and promote well-being for all at all ages, which identifies major deficits in global healthcare and provides a roadmap to correct these deficits (https://sdgs.un.org/2030agenda).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".