Low stick number polygons representing all knot types through 13 crossings
Bibliographic record
Abstract
This dataset contains low stick number representatives for all knot types. For knot types where the stick number was known at the time of deposit, these polygons match the minimal stick number. The file stick-number-bounds.nc is a NetCDF file containing stick data on all knots through 13 crossings. Each knot type has 4 pieces of data associated to it: number of sticks, number of crossings, vertex coordinates, and PD code of the minimal stick representative. So for the knot 10_37 these are located at 10_37/sticks, 10_37/crossings, 10_37/coords, and 10_37/pdcode. Note that PD codes are 0-indexed and knot names follow the Rolfsen convention through 10 crossings (8_18, 9_29, etc.) and the Dowker–Thistlethwaite convention for knots with 11–13 crossings (K11a367, K12n242, etc.). For knots through 12 crossings, the vertex coordinates are also given in a separate tab-separated file. Just search for the knot name below.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.115 |
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".