Design and Implementation of a Web-based Multi-user Data Analysis Environment for a Vancouver Island Drug-checking Initiative
Bibliographic record
Abstract
This project provides the detailed design and implementation details of a web-based multi-user data analysis and visualization environment for a Vancouver Island drug-checking initiative, along with the design of an accompanying website. It also includes the data analysis performed to answer some initial research questions. The drug-checking project collects analytical chemical data and survey data during the process for detailed analysis and data mining. The web-based multi-user data analysis environment enables people interested in analysis of drug-checking data to effectively collaborate using this database and also allows them to access all the required scientific, data analysis tools and libraries. The website is intended to communicate the results and findings to the public for harm reduction. It displays the aggregate results from the data and features interactive data visualization to educate users about component mixture analysis. It also provides information about the drug-checking program to different stakeholders including users of drug-checking service, chemists, social workers, harm reduction workers, pharmacists, and those interested in further developing the instrumental methods. It provides information about mission and goals of the project, services offered, research aspects of the project, technologies used and some frequently asked questions.
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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".