Bibliographic record
Abstract
Over the past several years an international community of scientists and engineers has emerged with a common goal to solve the technical challenge required to construct a giant radio telescope with a collecting area of one square kilometre. The Square Kilometre Array (SKA) will have a hundred times more collecting area than our most powerful existing radio telescopes, providing sensitivity of a few tens of nanoJy in the centimetre/decimetre wavelength continuum. With a spatial resolution better than the Hubble Space Telescope, a field of view larger than the full moon, and the ability to simultaneously image a wide range of red shift, the SKA will be the worlds premier spectroscopic imaging telescope at any waveband. At long wavelengths the SKA will be able to detect emission from atomic hydrogen gas at extreme redshifts, allowing study of the “Dark Ages” of the Universe, before, and during, the transition phase when the initial stars formed and reionization occurred. The combination of sensitivity, wide field of view and high angular resolution, will allow high resolution imaging of the interstellar media and magnetic field of a vast number of galaxies to high redshift. Measurements of atomic hydrogen emission and continuum emission will trace the star formation history of the Universe from primordial galaxies to the present.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".