A Web-Based System for Mapping Laboratory Networks: Analysis of GLaDMap application
Bibliographic record
Abstract
Public health emergencies such as H1N1 and SARS pandemics have demonstrated and validated the necessity of a strong and cohesive laboratory response system that is able to respond to threats in an efficient and timely manner. Individual laboratories, through connection with other laboratories or networks, are able to enhance their capacity for preparedness and response to emergencies. Efficient networks often establish standards and maintain best practices within member laboratories. The Global Laboratory Directory Mapping tool (GLaDMap) supports the efforts of laboratory networks to improve their connectivity by providing a simple and efficient tool to profile laboratories by geographic location, function or expertise. The purpose of this paper is to evaluate the effectiveness of the GLaDMap search tool and the completeness of the descriptive content of networks and laboratories that are currently contained within the GLaDMap database. We determined the extent of information volunteered and how the system is being used. Although the system aims to attract an array of users from around the globe, our analysis reveals minimal participation and information sharing and that the low profile participation rate limits the tool's functionality. The Global Laboratory Directory platform has addressed barriers to participation by adding optional functionality such as restricted access to laboratory profiles to protect private information and by implementing additional functional applications complementary to GLaDMap.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".