A Platform For Rapid Deployment Of Mobile Asset Management Systems”. XXth ISPRS Congress
Bibliographic record
Abstract
In recent years, the convergence of location, information management and communication technologies have created an emerging market known as location-based service (LBS). LBS is a critical enabling technology using location as a filter to extract relevant information to provide value-added services. Mobile Asset Management System (MAMS) is one such service and has been rapidly gaining attention from corporations and individuals. A MAMS offers timely and relevant information necessary for informed decisions on efficient asset management, increasing productivity, profitability, safety and security. Despite there being a diverse array of potential applications, all MAMS share common elements such as data collection from remote assets, wireless communication for transmitting data from the assets to a central office back-end for storage, and software application to provide services to interested users. If these elements are recreated for every new MAMS, as is generally the case today, then significant time and resources will be wasted through duplication. Trying to tie the heterogeneous components into a MAMS has been a challenge for LBS developers. To overcome these obstacles, technologies that provide the common elements and fundamental functions have been investigated and developed at The University of Calgary. Heterogeneous components such as sensors, wireless networks and databases have been integrated into a single Development Platform which can become a foundation and is an innovative solution to numerous and diverse MAMS system development and for other LBS applications. By featuring an object-oriented, extensible and modular architecture, developers can choose the functions from the platform to use in their applications, extend or customize other functions and add their own specialized software applications when necessary. Technical details of the platform will be described along with field test results
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.041 |
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".