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REVIEW 3 major objections 5 minor 22 references

Machine learning the arithmetic of Boyd's Mahler measure conjectures

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Pith's one-line read The rational factor in Boyd's Mahler-measure identities is arithmetically structured: for p≥5 its p-adic valuation obeys a geometric law, and at 2 and 3 it is tied to the elliptic curve's bad primes.

desk verdict Valuable dataset and some real conjectures, but the headline p≥5 law is contradicted by the paper's own p=5 chi-square, and the 19-digit rational recognition deserves a hard look before the statistics are trusted. read the letter →

arxiv 2608.00615 v1 pith:GZ2W7U7Y submitted 2026-08-01 math.NT

classification math.NT MSC 11G0511F6711G40
keywords MahlermeasureBoydconjectureellipticcurvesL-functionspecialvaluesp-adicvaluationsBloch–Katoarithmeticstatisticsmachinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that the rational factor $r_k$ in Boyd's conjectural formula—Mahler measure of a one-parameter family equals $r_k$ times the $L'$-value of an associated elliptic curve—is not an arbitrary rational number but carries systematic arithmetic structure. Working with the first 250,000 integers $k$, the authors find that the reciprocal $n_k = 1/r_k$ is almost always an integer, that its size is governed by the conductor, and that its p-adic valuations obey distinct laws for $p\ge 5$, $p=3$, and $p=2$. For $p\ge 5$ the data indicate $P(v_p(n_k)=r)\approx p^{-r}$; for $p=3$ the valuation is tied to congruence classes of $k$ and the parity of a count of bad primes; for $p=2$ the valuation is approximately a weighted count of odd prime divisors of $k$ and $k^2-16$ plus a small negative-binomial residual. The paper states a precise lower-bound conjecture $\hat{v}(k)\le v_2(n_k)$ and proves it conditionally, under Bloch–Kato hypotheses, on an infinite subfamily.

What carries the argument

The central object is the reciprocal $n_k = 1/r_k$ of Boyd's rational factor. The main working identity is the empirical decomposition at the prime 2: $v_2(n_k) = \omega_{odd}(k) + 2\omega_{odd}(k^2-16) - c(k) + s(k) + X_k$, where $\omega_{odd}$ counts distinct odd prime divisors, $c(k)$ is 4 or 2 depending on whether $E_k$ is semistable or additive at 2, $s(k)$ is a deterministic correction that spikes when $k$ is 2-adically close to $\pm 8$, and $X_k$ follows $NB(3,3/5)$. At the prime 3 the analogous mechanism is the parity law $v_3(n_k)=0 \Rightarrow \alpha(k)$ odd and $v_3(n_k)=1 \Rightarrow \alpha(k)$ even, with $\alpha(k) = \#S_k + v_3(k) + 1_{8|k}$. At primes $p\ge 5$ the mechanism is the geometric law $P(v_p(n_k)=r)=p^{-r}$. The load-bearing bridge to arithmetic is the Bloch–Kato

What would settle it

Recompute $\mu_k$ and $L'(E_k,0)$ for 500 randomly chosen $k$ near 250,000 with 100-digit precision and rerun rational reconstruction; if any recovered $r_k$ differs from the 19-digit value, or if the reconstruction failure rate grows with the conductor, the empirical p-adic laws collapse. A complementary check: tally $v_5(n_k)$ on a fresh 250,000-value window, since the reported $\chi^2/\nu\approx 12.8$ for $p=5$ already flags this prime as the outlier.

Watch

Extended reading notes

Core claim

Boyd's conjecture asserts $\mu_k = r_k L'(E_k,0)$ for an elliptic curve $E_k$. The paper's central discovery: for all but seven exceptional $k$ up to 250,000, $r_k$ is the reciprocal of an integer $n_k$ whose p-adic valuations are locally determined by $E_k$. For $p\ge 5$, $P(v_p(n_k)=r)=p^{-r}$. For $p=3$, valuations obey congruence and parity laws tied to bad primes $\equiv 1 \pmod{3}$. For $p=2$, $v_2(n_k) \approx \omega_{odd}(k)+2\omega_{odd}(k^2-16)-c(k)+s(k)+X_k$ with $X_k\approx NB(3,3/5)$. The paper conjectures $\hat{v}(k)\le v_2(n_k)$ for $k\ne 4,8$ and proves it conditionally, under Bloch–Kato hypotheses, on an infinite subfamily $k=4u$.

Load-bearing premise

The whole dataset depends on recognizing $r_k$ as a rational number from 19 decimal digits of $\mu_k$ and $L'(E_k,0)$; if that precision is insufficient for large $k$, some of the 250,000 quotients are misidentified, and every valuation law inherits the error.

Editorial extensions

If this is right

  • If the p≥5 law holds, an integer k has n_k divisible by p with probability 1/(p−1), not 1/p, and higher powers of p then follow the classical geometric distribution.
  • If Conjecture 3.2 is true, v_2(n_k) is always at least the weighted count of odd bad primes minus a bounded 2-adic correction; in particular its average order is at least 5 log log k.
  • Under Bloch–Kato, valuations of n_k carry information about Tate-twisted Selmer groups and regulator indices; conditional verification on the k=4u family shows Mahler-measure data can certify lower bounds on Selmer 2-ranks.
  • The near-integrality statement (n_k integer except k∈{3,4,5,8,12,16,32}) is recovered and sharpened, and the size law |n_k| ≍ N_k/log k gives a first-order prediction of the magnitude of n_k.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The p=5 χ² deviation, while small, is a signature that the p≥5 law may fail at small primes; a natural test is to recompute the p=5 tallies on a disjoint 250,000-value window or under the nonsquare k=√m regime.
  • If the residual X_k is genuinely NB(3,3/5) and independent of k, then the sharpness cases v_2(n_k)=\hat v(k) should have density dictated by that distribution; the network's improved detection of those cases suggests a deterministic hidden feature—possibly a Selmer 2-rank or regulator index equal to 1—that a dedicated classifier could try to isolate.
  • The same valuation statistics could be tested on other one-parameter Mahler-measure families to see whether the 2-adic weighted count and negative-binomial residual are universal or special to this family.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper computes Boyd Mahler measure quotients r_k (equivalently n_k = 1/r_k) for the first 250,000 integer k, combining PARI/GP numerical computation with LLL rational recognition, statistical analysis, and transformer experiments on the Axolver/Int2Int platform. The main statistical claims are: the sign of r_k is governed by the root number; |n_k| has order N_k/log(k) via Lemma 3.1; for p≥5 the positive p-adic valuations of n_k follow P(v_p(n_k)=r)=p^{-r}; the 3-adic valuations obey congruence and parity laws on large subfamilies; and the 2-adic valuations are modeled by a weighted count of odd primes dividing k and k^2-16, plus corrections c(k), s(k), and a negative-binomial residual. A conditional proof of the 2-adic lower-bound conjecture (Conjecture 3.2) is given for a special family under Bloch-Kato-type assumptions from [DGdJK26]. The machine-learning experiments show that the model can learn the size of n_k from the conductor, partially learn v_3 and v_2, but collapses to the trivial prediction for p=5,7.

Significance. If the empirical regularities survive scrutiny, this is a substantial contribution to the arithmetic of Boyd's Mahler measure conjectures: it proposes concrete, locally structured laws for the rational factors r_k and connects them to conductors, bad reduction, and Bloch-Kato Selmer groups. The paper's strengths are its scale, the explicit chi-square diagnostics, the public data/code, and the honest reporting of the neural-network limitations (e.g., §5.2). Lemma 3.1 is clean and the conditional Proposition 3.4 is a serious attempt to connect the data to the Bloch-Kato framework. However, the headline p≥5 law is already contradicted by the paper's own Table 3.1 at p=5, and the rational-recognition step at only 19 decimal digits is not validated for the largest conductors. Both points are load-bearing for the empirical claims and require correction before the paper's central assertions can be accepted.

major comments (3)
  1. [§3.2, Table 3.1; Abstract] The unqualified law P(v_p(n_k)=r)=p^{-r} for all p≥5 is contradicted by the paper's own data at p=5. Table 3.1a reports χ²=76.84 with ν=6, i.e. χ²/ν=12.81, and Table 3.1b shows systematic excesses at r=2,3,4,6 (ratios 1.065, 1.071, 1.178, 1.188). Since p=5 has by far the largest sample in the table, this is not a tail artifact. The abstract, §3.2, and §6 all retain the unqualified p≥5 statement. The paper must either state the law only for p≥7, or formulate and test a p=5 refinement, e.g. conditioning on p|k, p|(k²−16), or the reduction type of E_k. As written, the headline empirical claim is false at p=5.
  2. [§2, numerical recognition] All valuation statistics in §3 inherit the correctness of the LLL rational recognition of r_k from 19 decimal digits. No higher-precision check is reported. For large k, n_k is of size ≍ N_k/log(k), and the corresponding rational r_k can have large numerator and denominator; 19 digits may not be sufficient to determine such a rational uniquely. A misidentified r_k would silently bias every empirical law in Section 3. Please recompute a random sample of values (especially large k and large conductors) with, say, 50–100 digits and report the fraction of r_k that change, or provide a denominator bound justifying 19 digits.
  3. [§3.3–3.4, Table 3.5, Figure 3.4] The statistical models in §3.3–3.4 are fitted and evaluated on the same 250,000 values: the weights W_q(k), corrections c(k) and s(k), the negative-binomial parameters, the mode shift in (3.12), and the exceptional sets in Table 3.5 are all estimated from the full dataset. No out-of-sample split or holdout validation is reported. Moreover, Figure 3.4 shows χ²/ν=7.9 for the final 2-adic model, which for N≈250,000 is a poor absolute fit despite the similar visual shape. Please add a validation protocol (e.g., fit on one half, test on the other), report out-of-sample χ² for the valuation distributions, and quantify the NB goodness-of-fit rather than relying on visual agreement.
minor comments (5)
  1. [Figure 3.4 caption] The caption reads 'χ 2/ν=7.9' with broken typography; define χ² and ν explicitly at that point, since the main definition appears only in §3.2.
  2. [§3.3.1, Table 3.3] Law (3.6) is stated to hold on A 'with the exception of k=1', but k=1 belongs to A; please clarify whether the exception is the single k=1 or a different value, and make the statement in (3.6) consistent with the table.
  3. [§5.1, Figure 5.2] The text says the model learned fastest with N_k, 'closely followed by factor(Δk) and closely followed by factor(Nk)'; the figure legend lists the factored conductor peak at 99.5% and factored discriminant at 99.7%. Please align the text with the plotted peaks.
  4. [§4] The validation set is described as not used for hyperparameter tuning, but model selection across epochs is performed on validation accuracy. Please note this explicitly, as it is a form of model selection.
  5. [Throughout] Several displayed equations have broken math spacing (e.g., 'N_k/4π^2 L(E_k,2)' in (3.3), and 'NB(3,3/5)' vs 'NB(r=3, p=3/5)'). A careful proofread of the LaTeX rendering would improve clarity.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor in-sample 'predictor' labeling in the 2-adic model; no load-bearing self-citation or definitional circularity.

  1. fitted input called prediction [Section 3.4, equations (3.8)-(3.12)]
    "Since the mode of this distribution is 1, the best deterministic predictor we found for v_2(n_k) is (3.12) P̂(k) := round(ˆv(k) + s(k) + 1). This predictor has Pearson correlation coefficient 0.78 with v_2(n_k), only slightly higher than the correlation obtained using ˆv(k) alone. Moreover, the prediction P̂(k) is correct 24.3% of the time."

    The terms entering P̂ are fitted to the same 249,999 values of v_2(n_k): the weights 1 and 2 in W_q(k) are described as 'empirically' appropriate, c(k) is chosen to raise the correlation from 0.69 to 0.77, s(k) is fit to the excess table, and the NB(3,3/5) residual is fit to the same histogram. Reporting the correlation and 24.3% accuracy of P̂ on this identical dataset is therefore a goodness-of-fit statistic, not an out-of-sample prediction. The 'prediction' is statistically forced by the fitted inputs; no held-out evaluation of P̂ is given.

full rationale

The paper is primarily an empirical study. Its central claims (the p≥5 valuation law, the 2-adic model, and the parity laws at 3) are presented as observations or heuristics, not as derivations, so fitting them to the dataset is not hidden circularity. The numerical derivation of n_k = 1/r_k uses independently computed Mahler measures and L'(E_k,0) with LLL rational recognition (Section 2), not a self-consistent ansatz. The conductor-size estimate (3.4) follows from the hypergeometric formula (2.1), the functional equation (3.3), and Lemma 3.1, all standard or proven in the paper; it is not obtained by fitting. The machine-learning experiments use a train/test split and are therefore out-of-sample, although using the 'test' set for monitoring is a methodological concern rather than circularity. Proposition 3.4 is explicitly conditional on the external preprint [DGdJK26] and on Bloch–Kato assumptions; that citation is not by the present authors, and the proof reduces Conjecture 3.2 for a special family to those assumptions rather than smuggling the conjecture in. Self-citations such as [Lal10] and [LR07] are to standard theorems and proven formulas, not load-bearing uniqueness claims. The paper itself flags the p=5 deviation and the heuristic nature of the 2-adic model, which we weigh as transparent limitation statements rather than circularity. The only genuine circularity-adjacent step is the in-sample accuracy claim for P̂(k) in Section 3.4, which is minor relative to the paper's central empirical content.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central empirical claims rest on six fitted parameters (weights, correction functions, NB parameters, mode shift, sign shift) plus standard analytic number theory and unproved Bloch-Kato-type hypotheses for the conditional theorem. No new entities are introduced.

free parameters (6)
  • W_q(k) weights = 1 for q|k, 2 for q|k^2-16
    Chosen to maximize Pearson correlation with v_2(n_k) (improving r from 0.69 to 0.77); Section 3.4.
  • c(k) correction = 4 if E_k semistable at 2 (v_2(N_k) in {0,1}), else 2
    Improves the correlation between v̂(k) and v_2(n_k); eq. (3.8).
  • s(k) correction for 2-adic proximity to ±8 = (3/2) * max(1 - 2^{5 - max(v2(k-8), v2(k+8))}, 0)
    Fitted to the excess G(k) = v_2(n_k) - v̂(k) grouped by 2-adic depth M (Table 3.6c); eq. (3.10).
  • Negative binomial parameters (r, p) for X_k = r=3, p=3/5, mean 2, mode 1
    Fit to the residual distribution after removing deterministic terms in the v_2 model; Section 3.4 after eq. (3.11).
  • Mode shift in P̂(k) = +1 (round of mode of fitted NB)
    Deterministic predictor P̂(k) = round(v̂(k) + s(k) + 1) uses the fitted mode; eq. (3.12).
  • ε(m) sign-dependent shift for the nonsquare case = 0 for |m| a perfect square, 1 for m < 0 nonsquare, 2 for m > 0 nonsquare
    Fitted so that v̂(m) ≤ v_2(n_k) holds in the k = √m dataset; Section 3.5.
assumptions (6)
  • domain assumption Boyd's conjectural identity (0.1): µ_k = r_k L'(E_k, 0) for k ∈ Z \ {-4, 0, 4}
    Used to define and numerically recognize r_k by dividing the computed Mahler measure by L'(E_k, 0); Sections 0 and 2.
  • standard math Modularity and functional equation for L(E_k, s), giving L'(E_k, 0) = ω_k N_k/(4π²) L(E_k, 2)
    Invoked in eq. (3.3) to derive |n_k| ≍ N_k/log k; standard for elliptic curves over Q by Wiles et al.
  • standard math Hasse-Weil bound |a_p| ≤ 2√p
    Used in the proof of Lemma 3.1 to bound local factors of L(E, 2).
  • domain assumption Hypergeometric formulas for µ_k from [RZ14, p. 2324]
    Eq. (2.1) is used to compute µ_k to high precision for all k in the dataset.
  • domain assumption Theorems and assumptions of [DGdJK26], including Assumption 12.1, finite-dimensionality of K_2(Ẽ_u)^int ⊗ Q, finiteness of H^1_f(Q, Ẽ_u[2^∞](-1)), and the Bloch-Kato conjecture
    Used in Proposition 3.4 and Remark 3.5 to prove Conjecture 3.2 conditionally; these are unproved conjectural inputs.
  • domain assumption Numerical correctness of PARI/GP's lindep (LLL) and hypergeometric evaluations at 19 decimal digits for all k
    The entire dataset depends on these computations; Section 2. No independent verification is reported.

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Cite this review

Pith. "Pith review of Machine learning the arithmetic of Boyd's Mahler measure conjectures." pith.science (2026). https://pith.science/paper/GZ2W7U7Y

@misc{pith2026260800615,
  author       = {Pith},
  title        = {Pith review of: Machine learning the arithmetic of Boyd's Mahler measure conjectures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GZ2W7U7Y}},
  note         = {Machine review of arXiv:2608.00615}
}
abstract

Boyd conjectured that the Mahler measure of $P_k(x,y)=x+y+\frac{1}{x}+\frac{1}{y}+k$ for $k$ an integer, is given by $r_kL'(E_k,0)$, where $E_k$ is the elliptic curve associated to the zero locus of $P_k$ and $r_k$ is a rational number. We study various arithmetic properties of $r_k$ using a dataset containing the first $250{,}000$ values of $k$, combining large-scale statistical analysis assisted by Claude with transformer-based experiments carried out using Axolver. We recover Boyd's observation that, apart from a few exceptions, $r_k$ is the reciprocal of an integer. The size of this integer is governed by the conductor of the elliptic curve. Moreover, its $p$-adic valuations display markedly different behavior according to the prime. For $p\geq 5$, the probability of $v_p(r_k)=-m$ for $m\geq 1$ appears to be $p^{-m}$. For the primes $2$ and $3$, however, we find additional arithmetic structure involving congruence conditions on $k$ and the primes of bad reduction of $E_k$. Although the neural networks do not predict $r_k$ exactly, they recover significant information about its magnitude and valuations. In particular, the experiments at the prime $2$ suggest arithmetic structure beyond the explicit predictor obtained from our statistical analysis.

Figures

Figures reproduced from arXiv: 2608.00615 by the authors.

Figure 3
Figure 3. we see the distribution of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 3.1
Figure 3.1. we see the distribution of L(Ek, 2) in our dataset. 0.6 0.8 1.0 1.2 1.4 1.6 1.8 L(Ek, 2) = 4 2 |nk| k/Nk 0 2000 4000 6000 8000 10000 count Distribution of L(Ek, 2) over all k (n = 249999) [PITH_FULL_IMAGE:figures/full_fig_p007_3_1.png] view at source ↗
Figure 3.2
Figure 3.2. The sets of [PITH_FULL_IMAGE:figures/full_fig_p013_3_2.png] view at source ↗
Figures from the paper (18 more)
Figure 3.3
Figure 3.3. Figure 3.3: v2(nk) vs ˆv(k) for k with v2(nk) ≥ 0 class count mean v2(nk) mean G(k) k odd 125,000 9.56 +2.05 v2(k) = 1 62,500 11.28 +2.04 v2(k) = 2 31,249 10.39 +2.00 v2(k) = 3 15,625 11.29 +2.41 v2(k) = 4 7,813 8.67 +1.86 v2(k) = 5 3,906 8.65 +1.91 v2(k) = 6 1,953 8.55 +1.90 v2…
Figure 3.4
Figure 3.4. Figure 3.4: The observed distribution of v2(nk) for values with v2(nk) ≥ 0, com￾pared with the prediction ˆv(k) + s(k) + NB(3, 3 5 ). where NB denotes the negative binomial distribution. Accordingly, in this probabilistic model, we take P(Xk = j) =  j + 2 2  3 5 3  2 5 j ,…
Figure 5
Figure 5. Figure 5: , which suggests that the model does not learn anything about the sign of [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 5.1
Figure 5.1. Figure 5.1: The model’s accuracy on the size of nk with ωk included versus not included. 0 50 100 150 200 250 300 350 epoch 30 40 50 60 70 80 90 100 size accuracy o n rk (%) Learning rk: k, k + extra features conductor Nk (peak 100.0%) factored conductor factor(Nk) (peak 99.5%) …
Figure 5.2
Figure 5.2. Figure 5.2: Learning rk from k and ωk in three variants: Giving Nk, factor(Nk) or factor(∆k). Nevertheless, we find it puzzling that factor(∆k) leads to faster learning than factor(Nk). The other considered features, namely the rank and the ap’s do not seem to influence the mode…
Figure 5.3
Figure 5.3. Figure 5.3: Distribution of the model guesses for L(Ek, 2) against the true values of L(Ek, 2), for k in the validation set. This is for the experiment where k, ωk and factor(∆k) are given as input. 0 50 100 150 200 250 300 350 400 epoch 10 15 20 25 30 35 40 45 50 validation gre…
Figure 5.4
Figure 5.4. Figure 5.4: The validation accuracy (i.e. the percentage of correct guesses by the model) as a function of the epochs. eventually predicts that none of the nk are multiple of p, thus leading to a greedy accuracy of p−2 p−1 . Therefore, we cannot extract any conclusions from thes…
Figure 5.5
Figure 5.5. Figure 5.5: The validation accuracy, conditioned on v3(nk) 0 50 100 150 200 250 300 350 400 epoch 0 20 40 60 80 100 P(m o d el correct v3) (%) Accuracy conditioned on v3(nk), restricted to k A v3 = 1 v3 = 0 v3 > 1 [PITH_FULL_IMAGE:figures/full_fig_p022_5_5.png]
Figure 5.6
Figure 5.6. Figure 5.6: The validation accuracy on k ∈ A, conditioned on v3(nk). and so trivially classifies the true v3(nk) = 1 cases correctly (this also explains the starting plateau in [PITH_FULL_IMAGE:figures/full_fig_p022_5_6.png]
Figure 5.7
Figure 5.7. Figure 5.7: Accuracy on v3(nk) ∈ {0, 1} over the sets of [PITH_FULL_IMAGE:figures/full_fig_p023_5_7.png]
Figure 5.8
Figure 5.8. Figure 5.8: The accuracy assuming v3(nk) ∈ {0, 1} outside C ′′ and k ≡ 0, ±5 mod 27. 0 50 100 150 200 250 300 epoch 10 15 20 25 30 35 40 validation greedy accuracy (%) Validation greedy accuracy vs. epoch greedy exact-match [PITH_FULL_IMAGE:figures/full_fig_p024_5_8.png]
Figure 5.9
Figure 5.9. Figure 5.9: The validation accuracy (i.e. the percentage of correct guesses by the model) as a function of the epochs. 5.4.1. Learning around the first plateau of [PITH_FULL_IMAGE:figures/full_fig_p024_5_9.png]
Figure 5.10
Figure 5.10. Figure 5.10: The proportion of k in the validation set for which the model predicts vˆ(k) ≤ v2(nk). be seen that the mass of the confusion matrix lies close to the diagonal. As the number of epochs grows further, the distribution spreads out from the diagonal even before overfit…
Figure 5.11
Figure 5.11. Figure 5.11: Confusion-matrix snapshots across training epochs, including the Pearson correlation [PITH_FULL_IMAGE:figures/full_fig_p026_5_11.png]
Figure 5.12
Figure 5.12. Figure 5.12: The Pearson correlation between the model predictions and our pre￾dictor Pˆ(k) for k in the validation set as a function of the epochs. 0 50 100 150 200 250 300 epoch 0 10 20 30 40 50 60 70 80 P(model correct | G(k) = 0) (%) Accuracy by excess G(k) = v2(nk) v(k) vs.…
Figure 5.13
Figure 5.13. Figure 5.13: The proportion of times the model is correct, given that v2(nk) = ˆv(k) as a function of the epochs. We found no substantial learning when introducing c2, Nk, or ∆k as features. For the last two cases, this is consistent with the difficulty in factorization. 6. Conc…
Figure 7.1
Figure 7.1. Figure 7.1: Histogram of the 3-adic valuations v3(nk) for 1 ≤ k ≤ 250K such that v3(nk) ≥ 0, where K = 1000 [PITH_FULL_IMAGE:figures/full_fig_p031_7_1.png]
Figure 7.2
Figure 7.2. Figure 7.2: Histogram of the 2-adic valuations v2(nk) for 1 ≤ k ≤ 250K such that v2(nk) ≥ 0, where K = 1000. k k − 4 k + 4 W(k) ˆv(k) v2(nk) v2(nk) − vˆ(k) 321559 3 · 5 · 13 · 17 · 97 11 · 23 · 31 · 41 21 17 19 +2 400201 3 · 7 · 17 · 19 · 59 5 · 13 · 47 · 131 21 17 20 +3 440891 …

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