Bibliographic record
Abstract
We present a catalog of 97 spectroscopic redshifts of z<1 galaxies in the Hubble Deep Field South (HDFS) and its Flanking Fields (FFs). In the HDFS proper, we observed approximately half the galaxies brighter than I(AB)=24 and obtained redshifts for 76% of them. Targets in our HDFS sample were pre-selected to be at z<1 based on photometric redshifts, while in the FFs a simple magnitude cut was used. The photometric redshift pre-selection in the HDFS resulted in a spectroscopic success rate that is significantly higher than in the FFs, where no pre-selection was applied. The RMS precision of our redshift measurements, determined from repeat observations, is delta_z=0.0003. We present the photometry and redshifts for the 97 objects for which we secured spectroscopic redshifts and describe the basic properties of this sample.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".