Design Theory and Some Non-simple Forbidden Configurations
Bibliographic record
Abstract
Let 1_k 0_l denote the (k+l)\times 1 column of k 1's above l 0's. Let q. (1_k 0_l) $ denote the (k+l)xq matrix with q copies of the column 1_k0_l. A 2-design S_λ(2,3,v) can be defined as a vx(λ/3)\binom{v}{2} (0,1)-matrix with all column sums equal 3 and with no submatrix (λ+1).(1_20_0). Consider an mxn matrix A with all column sums in {3,4,... ,m-1}. Assume m is sufficiently large (with respect to λ) and assume that A has no submatrix which is a row permutation of (λ+1). (1_2 0_1). Then we show the number of columns in A is at most (λ)/3)\binom{m}{3} with equality for A being the columns of column sum 3 corresponding to the triples of a 2-design S_λ(2,3,m). A similar results holds for(λ+1). (1_2 0_2). Define a matrix to be simple if it is a (0,1)-matrix with no repeated columns. Given two matrices A, F, we define A to have F as a configuration if and only if some submatrix of A is a row and column permutation of F. Given m, let forb(m,q.(1_k 0_l)) denote the maximum number of possible columns in a simple m-rowed matrix which has no configuration q.(1_k 0_l). For m sufficiently large with respect to q, we compute exact values for forb(m,q.(1_1 0_1)), forb(m,q.(1_2 0_1)), forb(m,q.(1_2 0_2)). In the latter two cases, we use a construction of Dehon (1983) of simple triple systems S_λ(2,3,v) for λ>1. Moreover for l=1,2, simple mxforb(m,q.(1_2 0_l)) matrices with no configuration q.(1_2 0_l) must arise from simple 2-designs S_λ(2,3,m) of appropriate λ. The proofs derive a basic upper bound by a pigeonhole argument and then use careful counting and Turan's bound, for large m, to reduce the bound. For small m, the larger pigeonhole bounds are sometimes the exact bound. There are intermediate values of m for which we do not know the exact bound.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".