Bibliographic record
Abstract
The elegant theory that underlies gravitational lensing phenomena makes it a powerful tool for exploring the large scale structure of the universe. ELTs bring improved angular resolution and faint source spectroscopy capabilities to gravitational lensing studies which will enble qualitatively new investigations. Probing background sources having more than a decade higher source density than current studies will take weak lensing measurements from the outskirts of individual clusters into the cosmic web. These measurements require imaging and spectroscopy of distant galaxies fainter than mAB ~ 27 mag (~50 nano-Jansky). Strong gravitational lensing magnifies background sources and will allow the study of individual unresolved sources at least one decade fainter in flux than the telescope will otherwise reach, providing exploratory studies for 100m class telescopes. Strongly lensed sources will allow spectroscopy at sub nano-Jansky source frame flux levels in about 106 seconds. The expected sources include globular clusters in formation and individual first light stars. The geometry of strong lensing will also become a powerful constraint on cosmological constants. In lensed sources it will be possible to measure source frame velocities at about the 500 km s-1 level. These science goals will require an AO capability in which the PSF shape can be mapped to a precision of 1-2% over a field of about 2 arc-minutes, an integral field-unit spectrograph capable of being deployed on arcs that are generally 10 arc-seconds long and astrometric precision at the level of 10's of micro arc-second.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".