The Linkcell Construct and Location-Aware Query Processing for Location-Referent Transactions in Mobile Business
Bibliographic record
Abstract
The technologies that enable the transactions and interactions of mobile business are now as ubiquitous as any business-applicable technology that has emerged in recent decades. There is also an exploding base of literature with mobile business as its subject. The variety and volume of literature present a challenge to defining mobile business (m-business) in a way that differentiates it from other forms of technology-enabled business activity. For purposes here, m-business is held to be an extension of electronic business wherein transactions occur through communication channels that permit a high degree of mobility by at least one of the transactional parties. Within m- business, the distinct sub-area of locationbased mobile business (l- business) has recently emerged and is rapidly expanding (Frost & Sullivan, 2006). In l-business, the technologies that support m-business transactions are extended to incorporate location-aware capabilities. A system is ‘location aware’ when it senses a transactional party’s geographical position and then uses that positional information to perform one or more of the CRUD (create, retrieve, update, delete) functions of data management in support of a mobile user’s transactional activities (Butz, Baus, and Kruger, 2000). In their discussion of “location awareness”, Yuan and Zhang (2003) suggest that it “is a new dimension for value creation” applicable to an extensive variety of areas in which mobility is a salient characteristic: travel and tourism, commercial transportation, insurance risk/recovery management, emergency response, and many others.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".