Building Web Processing Services with Birdhouse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The Web Processing Service (WPS) is an OGC interface standard to provide processing tools as Web Service.<br>The WPS interface standardizes the way processes and their inputs/outputs are described,<br>how a client can request the execution of a process, and how the output from a process is handled.</p><p>Birdhouse tools enable you to build your own customised WPS compute service<br>in support of remote climate data analysis.</p><p>Birdhouse offers you:</p><ul><li>A Cookiecutter template to create your own WPS compute service.</li> <li>An Ansible script to deploy a full-stack WPS service.</li> <li>A Python library, Birdy, suitable for Jupyter notebooks to interact with WPS compute services.</li> <li>An OWS security proxy, Twitcher, to provide access control to WPS compute services.</li> </ul><p>Birdhouse uses the PyWPS Python implementation of the Web Processing Service standard.<br>PyWPS is part of the OSGeo project.</p><p>The Birdhouse tools are used by several partners and projects.<br>A Web Processing Service will be used in the Copernicus Climate Change Service (C3S) to provide subsetting<br>operations on climate model data (CMIP5, CORDEX) as a service to the Climate Data Store (CDS).<br>The Canadian non profit organization Ouranos is using a Web Processing Service to provide climate indices<br>calculation to be used remotely from Jupyter notebooks.</p><p>In this session we want to show how a Web Processing Service can be used with the Freva evaluation system.<br>Freva plugins can be made available as processes in a Web Processing Service. These plugins can be run<br>using a standard WPS client from a terminal and Jupyter notebooks with remote access to the Freva system.</p><p>We want to emphasise the integrational aspects of the Birdhouse tools: supporting existing processing frameworks<br>to add a standardized web service for remote computation.</p><p>Links:</p><ul><li>http://bird-house.github.io</li> <li>http://pywps.org</li> <li>https://www.osgeo.org/</li> <li>http://climate.copernicus.eu</li> <li>https://www.ouranos.ca/en</li> <li>https://freva.met.fu-berlin.de/</li> </ul>
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.
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 | 0.000 | 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 it