Retrieval Optimization for Server-Based Repositories in Location-Based Mobile Commerce
Bibliographic record
Abstract
Location-based mobile commerce (LBMC) incorporates location-aware technologies, wire-free connectivity, and server-based repositories of business locations to support the processing of location-referent transactions (LRTs) between businesses and mobile consumers. LRTs are transactions in which the location of a business in relation to a consumer’s actual or anticipated location is a material transactional factor. Providing adequate support for LRTs requires the timely resolution of queries bearing transaction-related locational criteria. The research reported here evaluates and extends the author’s location-aware method (LAM) of resolving LRT-related queries. The results obtained reveal LAM’s query resolution behavior in a variety of simulated LBMC circumstances and confirm the method’s potential to improve the timeliness of transactional support to mobile consumers. The article also identifies and evaluates a heuristic useful in maintaining optimal query resolution performance as changes occur in the scale and scope of server-based repositories.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.045 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".