PyWPS: overview, new features in version 4 and existing implementations
Bibliographic record
Abstract
Abstract PyWPS 4 is a re–make of the Python implementation of the WPS standard. It is the result of the work of over a dozen individual contributors, during a period of almost three years. One of the goals driving this re–implementation was to embrace modern Python technologies and the possibilities they open.This technical note reviews some of the more advanced possibilities this new PyWPS implementation opens. Request activity is now logged into a structured database, relying on a generic Object–Relational Mapping engine. The adoption of WSGI (Web Server Gateway Interface) opens new ways for load balancing request execution and application encapsulation, that are exemplified with modern Python technologies. Furthermore, PyWPS 4 is designed with containerisation in mind, expediting both development and deployment and improving security.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".