Bibliographic record
Abstract
A hybrid method for plotting 2-dimensional curves, defined implicitly by equations of the form f(x,y) = 0 is presented. The method is extremely robust and reliable and consists of Space Covering techniques, Continuation principles and Interval analysis (i.e. SCCI). The space covering, based on iterated subdivision, guarantees that no curve branches or isolated curve parts or even points are lost (which can happen if grid methods are used). The continuation method is initiated in a subarea as soon as it is proven that the subarea contains only one smooth curve. Such a subarea does not need to be subdivided further so that the computation is accelerated as far as possible with respect to the subdivision process. The novelty of the SCCI-hybrid method is the intense use of the implicit function theorem for controlling the steps of the method. Although the implicit function theorem has a rather local nature, it is empowered with global properties by evaluating it in an interval environment. This means that the theorem can provide global information about the curve in a subarea such as existence, non-existence, uniqueness of the curve or even the presence of singular points. The information gained allows the above-mentioned control of the subarea and the decision of its further processing, i.e. deleting it, subdividing it, switching to the continuation method or preparing the plotting of the curve in this subarea. The curves can be processed mathematically in such a manner, that the derivation of the plotted curve from the exact curve is as small as desired (modulo the screen resolution).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".