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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".