Exploring the Formation of Gravitationally Bound Objects Across the Universe
Bibliographic record
Abstract
From brown dwarfs to supermassive black holes, many of the strangest objects known to astronomers are formed as material collapses in on itself under its own gravity. Through a combination of physical theories and the latest computer simulations, Dr Shantanu Basu at the University of Western Ontario in Canada is offering intriguing new insights into how these structures originate. His theories could help astronomers to understand the very earliest stages of bodies ranging from those too small to become stars, to the vast, all-devouring heavyweights that reside in the centres of galaxies. Both diabetic adults and premature babies are at risk for a similar type of eye disease that involves the growth of abnormal, blood vessels in the retina, the photosensitive layer of the eye. When this eye disease occurs in diabetics, it is called diabetic retinopathy and when it occurs in premature infants, it is called retinopathy of prematurity. The pathologic vessels, seen in both of these diseases, can pull on the retina and cause it to detach, leading to blindness. Dr Tammy Movsas (Executive Director and Principal Investigator) and Dr Arivalagan Muthusamy (Chief Scientist) at the Zietchick Research Institute, USA, are developing new therapeutics to treat these serious retinal diseases that affect both premature baby eyes and mature adult eyes, such as those of diabetic women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".