The CatchMod toolbox: easy and guided access to ICT tools for Water Framework Directive implementation
Bibliographic record
Abstract
Web-based toolboxes are handy tools to inform experienced users of existing software in their disciplines. However, for the implementation of the Water Framework Directive, a much more diverse public (water managers, consultancy firms, scientists, etc.) will ask for a very wide diversity of Information and Communication Technology (ICT) tools. It is obvious that the users of a web-based ICT-toolbox providing all this will not be experts in all of the disciplines and that a toolbox for ICT tools for Water Framework Directive implementation should thus go beyond just making interesting web-links. To deal with this issue, expert knowledge is brought to the users through the incorporation of visitor-geared guidance (materials) in the Harmoni-CA toolbox. Small workshops of expert teams were organized to deliver documents explaining why the tools are important, when they are required and what activity they support/perform, as well as a categorization of the multitude of available tools. An integration of this information in the web-based toolbox helps the users to browse through a toolbox containing tools, reports, guidance documents and interesting links. The Harmoni-CA toolbox thus provides not only a virtual toolbox, but incorporates a virtual expert as well.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.101 | 0.037 |
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".