Bibliographic record
Abstract
This paper is based on the article “The self-counting identity”, published in the Fibonacci Quarterly in May 2017, vol. 55 and can be considered as its continuation.In the beginning, we define the “self-counting flow Φ”, which represents a tool for getting from one positive integer sequence to a corresponding other one. It is -so to saya flow on all positive integer sequences and thereby the self-counting sequence {ak } ∞ k=1 = {1, 2, 2, 3, 3, 3, 4, 4, 4, 4, …} shows itself as a unique fixed point.Various methods allow us to study the properties of the flow Φ such as its trajectories and the attraction of its fixed point. We also examine whether the self-counting sequence {a k } ∞ k=1 is the point of convergence of each positive integer sequence under a repeated application of the self-counting flow Φ.At the end of this article, we show some properties of other flows on positive integer sequences, for example those of the “Fibonacci flow F”.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".