Franklin Squares: A Chapter in the Scientific Studies of Magical Squares
Why this work is in the frame
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Bibliographic record
Abstract
Several aspects of magic(al) squares studies fall within the computational universe. Experimental computation has revealed patterns, some of which have lead to analytic insights, theorems, or combinatorial results. Other numerical experiments have provided statistical results for some very difficult problems. While classical nth order magic squares with the entries 1..n² must have the magic sum for each row, column, and the main diagonals, there are some interesting relatives for which these restrictions are increased or relaxed. These include: serial squares of all orders with sequential filling of rows which are always pandiagonal (i.e., having all parallel diagonals to the main ones on tiling with the same magic sum, also called broken diagonals); pandiagonal logic squares of orders 2n derived from Karnaugh maps; Franklin squares of orders 8n which are not required to have any diagonal properties, but have equal half row and column sums and 2-by-2 quartets; as well as sets of parallel magical bent diagonals. Our early explorations of magic squares, considered as square matrices, used Mathematica® to study their eigenproperties. We have also studied the moment of inertia and multipole moments of magic squares and cubes (treating the numerical entries as masses or charges), finding some elegant theorems. We have also shown how to easily compound smaller squares into very high order squares. There are patents proposing the use of magical squares for cryptography. Other possible applications include dither matrices for image processing and providing tests for developing constraint satisfaction problem (CSP) solvers for difficult problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it