Bibliographic record
Abstract
Graphic patterns in knitting are composed of relatively large pixels and create a picture when seen from a distance, while on close viewing the image falls apart into its constituent stitches. Such patterns are constrained in use of colors due to the nature of the medium and in spacing between pixels as a durability concern and are a challenge to create. This paper shows how to convert an arbitrary line-drawing or photograph to a constraint-compliant Fair-Isle knitting pattern for a programmable knitting machine or a manual knitter by formulating it as a Constraint Satisfaction Problem (CSP). First we generate a constraint-inconsistent starting pixel assignment. Then we produce a perceptually similar constraint compliant solution, by minimizing and randomly distributing pixel flips to preserve gestalt features of the original design. We evaluate ways of generating a starting assignment using thresholding and dithering and of solving the problem using pseudo-random texturing and search: Random Walk, GSAT and Min-Conflict. Two hybrid solutions that achieve an improved design-dependent result are described. To test the algorithms an interactive knitting pattern generator was implemented.
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 | 0.001 | 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".