Bibliographic record
Abstract
Abstract We define a new class of rings parameterized by binary forms of a certain type and give an effective lower bound for the number of such rings whose discriminant is less than a bound $X$. We also obtain a lower bound for the number of number fields whose ring of integers is in the above class and whose discriminant is less than a bound $X$. Our results improve the estimate of Bhargava–Shankar–Wang in [7]. In particular, we show the following: •When $n\ge 4,$ the number of rings of rank $n$ over $\mathbb{Z}$ with discriminant less than or equal to $X$ is $$ \begin{align*} & \gg_n X^{\frac{1}{2}+\frac{1}{n-\frac{4}{3}}}. \end{align*} $$•When $n\ge 6,$ the number of number fields of degree $n$ with discriminant less than $X$ is $$ \begin{align*} & \gg_{n,\epsilon} X^{\frac{1}{2} +\frac{1}{n-1} + \frac{(n-3)r_n}{(n-2)(n-1)}-\epsilon}, \end{align*} $$where $r_{n}=\frac{\eta _{n}}{n^{2}-4n+3-2\eta _{n} (n + \frac{2}{(n-2)})}$ and where $\eta _{n}$ is $\frac{1}{5n}$ if $n$ is odd and is $\frac{1}{88n^{6}}$ when $n$ is even.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".