Some computations on the spectra of Pisot and Salem numbers
Bibliographic record
Abstract
Properties of Pisot numbers have long been of interest. One line of questioning, initiated by Erdős, Joó and Komornik in 1990, is the determination of l ( q ) l(q) for Pisot numbers q q , where \[ l ( q ) = inf ( | y | : y = ϵ 0 + ϵ 1 q 1 + ⋯ + ϵ n q n , ϵ i ∈ { ± 1 , 0 } , y ≠ 0 ) . l(q) = \inf (|y|: y = \epsilon _0 + \epsilon _1 q^1 + \cdots + \epsilon _n q^n, \epsilon _i \in \{\pm 1, 0\}, y \neq 0). \] Although the quantity l ( q ) l(q) is known for some Pisot numbers q q , there has been no general method for computing l ( q ) l(q) . This paper gives such an algorithm. With this algorithm, some properties of l ( q ) l(q) and its generalizations are investigated. A related question concerns the analogy of l ( q ) l(q) , denoted
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".