International Space Station Benefits for Humanity
Bibliographic record
Abstract
The ISS partnership has seen a substantial increase in research accomplished, crew efforts devoted to research, and results of ongoing research and technology development. The ISS laboratory is providing a unique environment for research and international collaboration that benefits humankind. Benefits come from the engineering development, the international partnership, and from the research results. Benefits can be of three different types: scientific discovery, applications to life on Earth, and applications to future exploration. Working across all ISS partners, we identified key themes where the activities on the ISS improve the lives of people on Earth--not only within the partner nations, but also in other nations of the world. Three major themes of benefits to life on earth emerged from our review: benefits to human health, education, and Earth observation and disaster response. Other themes are growing as use of the ISS continues. Benefits to human health range from advancements in surgical technology, improved telemedicine, and new treatments for disease. Earth observations from the ISS provide a wide range of observations that include: marine vessel tracking, disaster monitoring and climate change. The ISS participates in a number of educational activities aimed to inspire students of all ages to learn about science, technology, engineering and mathematics. To date over 63 countries have directly participated in some aspect of ISS research or education. In summarizing these benefits and accomplishments, ISS partners are also identifying ways to further extend the benefits to people in developing countries for the benefits of humankind.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.884 | 0.859 |
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".