Bibliographic record
Abstract
Recent trends show that developers behind some of the most popular web mapping libraries put excessive work into creating custom hardware-accelerated rendering engines. Other libraries focus on functionality rather than visualization. From the perspective of the developer using these libraries an important question arises: is it necessary to use a WebGL-powered library for 2D web mapping? The answer was found through the implementation and evaluation of a simple WebGL renderer for the open source Web mapping library OpenLayers. It extends the previous, texture-based implementation with line-string, polygon, and label-rendering capabilities. Through various benchmarks, the benefits of using a WebGL rendering engine over the traditional, but nowadays widely supported and – in most cases – hardware-accelerated HTML5 Canvas renderer are assessed. Contrary to the current trends in Web mapping, results suggest that using the Canvas Application Programming Interface (API) is sufficient for smaller Web maps (up to around 2000 features and 60,000 vertices) using static vector data. WebGL only gives a noticeable performance boost with maps using large vector layers, such as Web GIS clients.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".