Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present weak lensing shear catalogues from the fourth data release of the Kilo-Degree Survey, KiDS-1000, spanning 1006 square \ndegrees of deep and high-resolution imaging. Our ‘gold-sample’ of galaxies, with well-calibrated photometric redshift distributions, \nconsists of 21 million galaxies with an effective number density of 6.17 galaxies per square arcminute. We quantify the accuracy of the \nspatial, temporal, and flux-dependent point-spread function (PSF) model, verifying that the model meets our requirements to induce \nless than a 0.1σ change in the inferred cosmic shear constraints on the clustering cosmological parameter S 8 = σ8 \n√ \nΩm/0.3. Through \na series of two-point null-tests, we validate the shear estimates, finding no evidence for significant non-lensing B-mode distortions \nin the data. The PSF residuals are detected in the highest-redshift bins, originating from object selection and/or weight bias. The \namplitude is, however, shown to be sufficiently low and within our stringent requirements. With a shear-ratio null-test, we verify the \nexpected redshift scaling of the galaxy-galaxy lensing signal around luminous red galaxies. We conclude that the joint KiDS-1000 \nshear and photometric redshift calibration is sufficiently robust for combined-probe gravitational lensing and spectroscopic clustering \nanalyses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it