Bibliographic record
Abstract
The distribution of real-time GIS (Geographic Information System) data among users is now more important than ever as it becomes increasingly affordable and important for scientific and government agencies to monitor environmental phenomena in real-time. A growing number of sensor networks are being deployed all over the world, but there is a lack of solutions for their effective monitoring. Increasingly, GIS users need access to real-time sensor data from a variety of sources, and the data must be represented in a visually-pleasing way and be easily accessible. In addition, users need to be able to collaborate with each other to share and discuss specific sensor data. The real-time acquisition, analysis, and sharing of sensor data from a large variety of heterogeneous sensor sources is currently difficult due to the lack of a standard architecture to properly represent the dynamic properties of the data and make it readily accessible for collaboration between users. This thesis will present a JEE-based publisher/subscriber architecture that allows real-time sensor data to be displayed collaboratively on the web, requiring users to have nothing more than a web browser and Internet connectivity to gain access to that data. The proposed architecture is evaluated by showing how an AJAX-based and a Flash-based web application are able to represent the real-time sensor data within novel collaborative environments. By using the latest web-based technology and relevant open standards, this thesis shows how map data and GIS data can be made more accessible, more collaborative and generally more useful.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".