Supplementary Tables for “Numerical Results on Class Groups of Imaginary Quadratic Fields”
Bibliographic record
Abstract
We present supplemental tables and additional data that extend that presented in [7]. Data corresponding to all the conjectures mentioned in [7] are included, and all tables are complete, including previously published results. In addition, two corrections to the data in [7] are included: – Originally, we only listed first occurrences of p-Sylow subgroups for primes p ≤ 173. In this paper, we present the entire list, for primes p ≤ 389. See Table 7. – When listing the first ∆ needing prime ideals of norm up to p, we pointed out an anomaly in the data at p = 181. Subsequent analysis has shown this to be a bug in our statistics gathering program. The data no longer contains any anomalies of this sort. See Table 15. Bounds on L(1, χ) There has been significant interest [2, 3, 6, 11] in the extreme values of L(1, χ∆) due to the relationship between it and the class number h∆. This can be seen in the analytic class number formula, L(1, χ∆) = h∆π where extreme values of L(1, χ∆) correspond to extreme values of h∆. In [10], Littlewood developed bounds on L(1, χ∆), namely that under the ERH, {1 + o(1)}(c1 log log ∆) −1 < L(1, χ∆) < {1 + o(1)}c2 log log(∆) , (0.1) where c1 and c2 are defined as follows: c1 = 12e γ /π 2 and c2 = 2e γ when 2 ∤ ∆ c1 = 8e γ /π 2 and c2 = e γ when 2 | ∆. ⋆ All three authors are supported in part by NSERC of Canada. In [11], Shanks investigated Littlewood’s bounds, and defined two values he termed the upper and lower Littlewood indices ULI = L(1, χ∆)/(c2 log log ∆) LLI = L(1, χ∆)c1 log log ∆. These indices effectively ignore the o(1) given in Littlewood’s bounds. We would expect extreme values of the LLI and the ULI to approach 1. Finally, as in [11], we define the function
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.615 | 0.201 |
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".