Bibliographic record
Abstract
and Canadian Space Agency (CSA), the James Webb Space Telescope sent back the first batch of deep space images on July 11, 2022. These images impress the world with their stunning clarify and detailed information captured within. Importantly, they remind us about our shared destiny in this boundless cosmos. For me, these images are as powerful as the last earth image taken by the Voyager 1 spacecraft on February 14, 1990, as she wondered into the space unknown to human. ''A Pale Blue Dot'' , Professor Carl Sagan's poetical description reoriented our sense of the world from planet earth to the boundless universe to think bigger and care more. Professor Sagan's another equally important message was his warning of the climate change at the US Congress in 1985. Unfortunately, efforts in tackling this challenge are at best inconsistent and more often unheeded in the past 30 years to give way to economic development and global competition. The ongoing impact of extreme weather around the world remains as one of the largest challenges for humanity in this century, and the only forward is through international cooperation and technological innovation. However, the old way of passive reaction after disasters no longer serves our needs, as painstakingly scouring through clues from the deep space is often infeasible and futile. Instead, we need to count on the power of machine intelligence to screen pictures of the universe, proactively monitor the environment, and precisely predict looming disasters throughout the world with minimal human intervention.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.201 | 0.128 |
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".